Graduate Course Descriptions
Highlighted courses are being taught in Fall 2008.
STAT 501 Experimental Statistics I (Banner Course Number: 50100)
Semester: Fall Spring Summer
Prerequisites: MATH 150, 151, 153 or equivalent.
Credits: 3
Primary Audience: Education, Social and Behavioral Sciences, but not Mathematical Sciences or Engineering.
Description: Applied statistics for students without calculus who anticipate the need to apply statistics in their future work. Covers some material not in STAT 301 and requires more work, though not more mathematical background. The SAS statistical software system is introduced and used. Intended primarily for graduate students in education and the social sciences. Not intended for students in the mathematical sciences or engineering.
*Credit should be allowed in no more than one of STAT 301, STAT 350, STAT 501, STAT 503 or STAT 511.
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Semester: Fall Spring Summer
Prerequisites: MATH 150, 151, 153 or equivalent.
Credits: 3
Primary Audience: Education, Social and Behavioral Sciences, but not Mathematical Sciences or Engineering.
Description: Applied statistics for students without calculus who anticipate the need to apply statistics in their future work. Covers some material not in STAT 301 and requires more work, though not more mathematical background. The SAS statistical software system is introduced and used. Intended primarily for graduate students in education and the social sciences. Not intended for students in the mathematical sciences or engineering.
*Credit should be allowed in no more than one of STAT 301, STAT 350, STAT 501, STAT 503 or STAT 511.
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STAT 502 Experimental Statistics II (Banner Course Number: 50200)
Semester: Spring Summer
Prerequisites: STAT 501 or equivalent
Credits: 3
Primary Audience: Primarily intended for students who have not had calculus. Not open to students in mathematical sciences or engineering.
Description: Second course in applied statistics, emphasizing design and analysis of both experiments and observational studies. Regression with several explanatory variables. Regression diagnostics. Analysis of variance for factorial designs. Multiple comparisons. Analysis of covariance. Repeated measures designs. Extensive use of the SAS statistical software. Can follow STAT 501 (or STAT 503 with some introductory SAS experience). Does not require knowledge of calculus. Good for graduate students in a variety of disciplines whose research will require statistical analysis. Not intended for students in the mathematical sciences or engineering.
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Semester: Spring Summer
Prerequisites: STAT 501 or equivalent
Credits: 3
Primary Audience: Primarily intended for students who have not had calculus. Not open to students in mathematical sciences or engineering.
Description: Second course in applied statistics, emphasizing design and analysis of both experiments and observational studies. Regression with several explanatory variables. Regression diagnostics. Analysis of variance for factorial designs. Multiple comparisons. Analysis of covariance. Repeated measures designs. Extensive use of the SAS statistical software. Can follow STAT 501 (or STAT 503 with some introductory SAS experience). Does not require knowledge of calculus. Good for graduate students in a variety of disciplines whose research will require statistical analysis. Not intended for students in the mathematical sciences or engineering.
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STAT 503 Statistical Methods for Biology (Banner Course Number: 50300)
Semester: Fall Spring Summer
Prerequisites: or Corequisite: Mathematical experience at the level of one semester of calculus is required.
Credits: 3
Primary Audience: Biology, pharmacy, some agriculture and health science.
Description: Extensive coverage of statistical methods for mature students. All examples and applications are drawn from the life, health and agricultural sciences. Excel statistical software is used. Mathematical experience at the level of one semester of calculus is required, though no calculus is used in the course.
*Credit should be allowed in no more than one of STAT 301, STAT 350, STAT 501, STAT 503 or STAT 511.
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Semester: Fall Spring Summer
Prerequisites: or Corequisite: Mathematical experience at the level of one semester of calculus is required.
Credits: 3
Primary Audience: Biology, pharmacy, some agriculture and health science.
Description: Extensive coverage of statistical methods for mature students. All examples and applications are drawn from the life, health and agricultural sciences. Excel statistical software is used. Mathematical experience at the level of one semester of calculus is required, though no calculus is used in the course.
*Credit should be allowed in no more than one of STAT 301, STAT 350, STAT 501, STAT 503 or STAT 511.
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STAT 506 Statistical Programming and Data Management (Banner Course Number: 50600)
Semester: Fall Spring
Prerequisites: An introductory Computer Science course equivalent to CS 158 or 154 or 180 and a calculus-based introductory statistics course such as STAT 350, STAT 503, or STAT 511
Credits: 3
Primary Audience: Undergraduates in statistics or actuarial science; graduate students in applied statistics or other disciplines.
Description: Offered on an irregular basis. Use of the SAS software system for managing statistical data. The SAS environment. Data description. Data access and management. SAS macro language and application development.
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Semester: Fall Spring
Prerequisites: An introductory Computer Science course equivalent to CS 158 or 154 or 180 and a calculus-based introductory statistics course such as STAT 350, STAT 503, or STAT 511
Credits: 3
Primary Audience: Undergraduates in statistics or actuarial science; graduate students in applied statistics or other disciplines.
Description: Offered on an irregular basis. Use of the SAS software system for managing statistical data. The SAS environment. Data description. Data access and management. SAS macro language and application development.
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STAT 511 Statistical Methods (Banner Course Number: 51100)
Semester: Fall Spring
Prerequisites: MA 162.
Credits: 3
Primary Audience: Students in engineering and science, and for any suitably prepared student planning to take STAT 512 or above.
Description: Applied statistics for students with a calculus background. Some probability theory is presented but applicable statistics is emphasized. May lead to STAT 512 or STAT 513. Taken by both undergraduate and graduate students from many subject areas, especially engineering and physical sciences.
*Credit should be allowed in no more than one of STAT 301, STAT 350, STAT 501, STAT 503 or STAT 511.
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Semester: Fall Spring
Prerequisites: MA 162.
Credits: 3
Primary Audience: Students in engineering and science, and for any suitably prepared student planning to take STAT 512 or above.
Description: Applied statistics for students with a calculus background. Some probability theory is presented but applicable statistics is emphasized. May lead to STAT 512 or STAT 513. Taken by both undergraduate and graduate students from many subject areas, especially engineering and physical sciences.
*Credit should be allowed in no more than one of STAT 301, STAT 350, STAT 501, STAT 503 or STAT 511.
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STAT 512 Applied Regression Analysis (Banner Course Number: 51200)
Semester: Fall Spring Summer
Prerequisites: STAT 503, 511 or 517.
Credits: 3
Primary Audience:
Description: Thorough applied course in regression and analysis of variance including experience with the SAS statistical software package. Topics include inference in simple and multiple linear regression, residual analysis, transformations, polynomial regression, model building with real data, nonlinear regression. One-way and two-way analysis of variance, multiple comparisons, fixed and random factors, analysis of covariance. Not mathematically advanced, but covers a large volume of material. Requires calculus, and simple matrix algebra is helpful. Recommended for graduate students and for hard working undergraduates from all areas.
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Semester: Fall Spring Summer
Prerequisites: STAT 503, 511 or 517.
Credits: 3
Primary Audience:
Description: Thorough applied course in regression and analysis of variance including experience with the SAS statistical software package. Topics include inference in simple and multiple linear regression, residual analysis, transformations, polynomial regression, model building with real data, nonlinear regression. One-way and two-way analysis of variance, multiple comparisons, fixed and random factors, analysis of covariance. Not mathematically advanced, but covers a large volume of material. Requires calculus, and simple matrix algebra is helpful. Recommended for graduate students and for hard working undergraduates from all areas.
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STAT 512Q Applied Regression Analysis (Banner Course Number: 51200)
Semester: Fall
Prerequisites: STAT 503, 511 or 517
Credits: 3
Primary Audience: Students enrolled through Engineering Professional Education
Description: Thorough applied course in regression and analysis of variance including experience with the SAS statistical software package. Topics include inference in simple and multiple linear regression, residual analysis, transformations, polynomial regression, model building with real data, nonlinear regression. One-way and two-way analysis of variance, multiple comparisons, fixed and random factors, analysis of covariance. Not mathematically advanced, but covers a large volume of material. Requires calculus, and simple matrix algebra is helpful. Recommended for graduate students and for hardworking undergraduates from all areas.
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Semester: Fall
Prerequisites: STAT 503, 511 or 517
Credits: 3
Primary Audience: Students enrolled through Engineering Professional Education
Description: Thorough applied course in regression and analysis of variance including experience with the SAS statistical software package. Topics include inference in simple and multiple linear regression, residual analysis, transformations, polynomial regression, model building with real data, nonlinear regression. One-way and two-way analysis of variance, multiple comparisons, fixed and random factors, analysis of covariance. Not mathematically advanced, but covers a large volume of material. Requires calculus, and simple matrix algebra is helpful. Recommended for graduate students and for hardworking undergraduates from all areas.
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STAT 513 Statistical Quality Control (Banner Course Number: 51300)
Semester: Fall Spring
Prerequisites: One semester of post-calculus statistics such as IE 230, MGMT 305, or STAT 511.
Credits: 3
Primary Audience:
Description: A survey of the major statistical tools used to enhance quality and productivity. The prerequisite is one semester of post-calculus statistics such as IE 230, MGMT 305, or STAT 511. Topics include control charts including adaptations, acceptance sampling for attributes and variables data, standard acceptance plans, sequential analysis, statistics of combinations; moments and probability distributions, applications.
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Semester: Fall Spring
Prerequisites: One semester of post-calculus statistics such as IE 230, MGMT 305, or STAT 511.
Credits: 3
Primary Audience:
Description: A survey of the major statistical tools used to enhance quality and productivity. The prerequisite is one semester of post-calculus statistics such as IE 230, MGMT 305, or STAT 511. Topics include control charts including adaptations, acceptance sampling for attributes and variables data, standard acceptance plans, sequential analysis, statistics of combinations; moments and probability distributions, applications.
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STAT 514 Design of Experiments (Banner Course Number: 51400)
Semester: Fall Spring
Prerequisites: STAT 512.
Credits: 3
Primary Audience:
Description: A thorough and practical course in design and analysis of experiments for experimental workers and applied statisticians. SAS statistical software is used for analysis. Taken by graduate students from many fields. Previous knowledge of SAS not required but helpful. Knowledge of regression helpful. Topics include design fundamentals, completely randomized design; randomized complete blocks; latin square; multiclassification; factorial; nested factorial; incomplete block and fractional replications for 2n ; 3n ; 2m 3n, confounding; 12 lattice designs; general mixed factorials; split plot; analysis of variance in regression models; optimum design.
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Semester: Fall Spring
Prerequisites: STAT 512.
Credits: 3
Primary Audience:
Description: A thorough and practical course in design and analysis of experiments for experimental workers and applied statisticians. SAS statistical software is used for analysis. Taken by graduate students from many fields. Previous knowledge of SAS not required but helpful. Knowledge of regression helpful. Topics include design fundamentals, completely randomized design; randomized complete blocks; latin square; multiclassification; factorial; nested factorial; incomplete block and fractional replications for 2n ; 3n ; 2m 3n, confounding; 12 lattice designs; general mixed factorials; split plot; analysis of variance in regression models; optimum design.
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STAT 514Q Design of Experiments (Banner Course Number: 51400)
Semester: Fall Spring
Prerequisites: STAT 512.
Credits: 3
Primary Audience:
Description: Students registering for this course need to contact the Engineering Professional Education office. A thorough and practical course in design and analysis of experiments for experimental workers and applied statisticians. SAS statistical software is used for analysis. Taken by graduate students from many fields. Previous knowledge of SAS not required but helpful. Knowledge of regression helpful. Topics include design fundamentals, completely randomized design; randomized complete blocks; latin square; multiclassification; factorial; nested factorial; incomplete block and fractional replications for 2n ; 3n ; 2m 3n, confounding; 12 lattice designs; general mixed factorials; split plot; analysis of variance in regression models; optimum design.
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Semester: Fall Spring
Prerequisites: STAT 512.
Credits: 3
Primary Audience:
Description: Students registering for this course need to contact the Engineering Professional Education office. A thorough and practical course in design and analysis of experiments for experimental workers and applied statisticians. SAS statistical software is used for analysis. Taken by graduate students from many fields. Previous knowledge of SAS not required but helpful. Knowledge of regression helpful. Topics include design fundamentals, completely randomized design; randomized complete blocks; latin square; multiclassification; factorial; nested factorial; incomplete block and fractional replications for 2n ; 3n ; 2m 3n, confounding; 12 lattice designs; general mixed factorials; split plot; analysis of variance in regression models; optimum design.
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STAT 515 Statistical Consulting Problem (Banner Course Number: 51500)
Semester: Fall Spring Summer
Prerequisites: MA 261, 172, or equivalent. Admission by consent of instructor. (May be taken several times for credit totaling up to seven.)
Credits: 1-3
Primary Audience:
Description: Requires Graduate students in Statistics report on a consultation problem involving a designed experiment or sample in which the student participates with members of the Department of Statistics staff.
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Semester: Fall Spring Summer
Prerequisites: MA 261, 172, or equivalent. Admission by consent of instructor. (May be taken several times for credit totaling up to seven.)
Credits: 1-3
Primary Audience:
Description: Requires Graduate students in Statistics report on a consultation problem involving a designed experiment or sample in which the student participates with members of the Department of Statistics staff.
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STAT 516 Basic Probability and Applications (Banner Course Number: 51600)
Semester: Fall Spring
Prerequisites: MA 261, 172, or equivalent.
Credits: 3
Primary Audience: Graduate Students in Science and Engineering
Description: An introduction to mathematical probability suitable as preparation for statistical theory (STAT 517) and mathematical modeling. General probability rules, conditional probability, discrete and continuous random variables, joint and conditional distributions, standard discrete and continuous families of distributions and their contexts, law of large numbers and central limit theorem.
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Semester: Fall Spring
Prerequisites: MA 261, 172, or equivalent.
Credits: 3
Primary Audience: Graduate Students in Science and Engineering
Description: An introduction to mathematical probability suitable as preparation for statistical theory (STAT 517) and mathematical modeling. General probability rules, conditional probability, discrete and continuous random variables, joint and conditional distributions, standard discrete and continuous families of distributions and their contexts, law of large numbers and central limit theorem.
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STAT 516Q Basic Probability and Applications (Banner Course Number: 51600)
Semester: Fall Spring
Prerequisites: MA 261, 172, or equivalent.
Credits: 3
Primary Audience: Graduate Students in Science and Engineering
Description: Students registering for this course need to contact the Engineering Professional Education office. An introduction to mathematical probability suitable as preparation for statistical theory (STAT 517) and mathematical modeling. General probability rules, conditional probability, discrete and continuous random variables, joint and conditional distributions, standard discrete and continuous families of distributions and their contexts, law of large numbers and central limit theorem.
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Semester: Fall Spring
Prerequisites: MA 261, 172, or equivalent.
Credits: 3
Primary Audience: Graduate Students in Science and Engineering
Description: Students registering for this course need to contact the Engineering Professional Education office. An introduction to mathematical probability suitable as preparation for statistical theory (STAT 517) and mathematical modeling. General probability rules, conditional probability, discrete and continuous random variables, joint and conditional distributions, standard discrete and continuous families of distributions and their contexts, law of large numbers and central limit theorem.
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STAT 517 Statistical Inference (Banner Course Number: 51700)
Semester: Fall Spring
Prerequisites: STAT 516 or 519 or equivalent.
Credits: 3
Primary Audience:
Description: A first course in the theory of statistics, to follow STAT 516. Covers some of the material of a first course in statistical methods, but with emphasis on theory, rather than practice. STAT 511 or other background in statistical methods is helpful. A basic estimation including unbiased, maximum likelihood and moment estimation; testing hypotheses for standard distributions and contingency tables; confidence intervals and regions; introduction to nonparametric tests and linear regression.
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Semester: Fall Spring
Prerequisites: STAT 516 or 519 or equivalent.
Credits: 3
Primary Audience:
Description: A first course in the theory of statistics, to follow STAT 516. Covers some of the material of a first course in statistical methods, but with emphasis on theory, rather than practice. STAT 511 or other background in statistical methods is helpful. A basic estimation including unbiased, maximum likelihood and moment estimation; testing hypotheses for standard distributions and contingency tables; confidence intervals and regions; introduction to nonparametric tests and linear regression.
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STAT 519 Introduction to Probability (MA 519) (Banner Course Number: 51900)
Semester: Fall Spring
Prerequisites: MA 510; or corequisite: MA 440 or 441
Credits: 3
Primary Audience:
Description: Algebra of sets, sample spaces, combinatorial problems, independence, random variables, distribution functions, moment generating functions, special continuous and discrete distributions, distribution of a function of a random variable, limit theorems.
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Semester: Fall Spring
Prerequisites: MA 510; or corequisite: MA 440 or 441
Credits: 3
Primary Audience:
Description: Algebra of sets, sample spaces, combinatorial problems, independence, random variables, distribution functions, moment generating functions, special continuous and discrete distributions, distribution of a function of a random variable, limit theorems.
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STAT 520 Time Series and Applications (Banner Course Number: 52000)
Semester: Spring
Prerequisites: STAT 516 and 511. Knowledge of some computer language or statistical package.
Credits: 3
Primary Audience:
Description: A first course in stationary time series with applications using real and simulated data. Computing projects are assigned, so some computer language (e.g. C, FORTRAN, SPLUS etc.) or statistical package should also be familiar. Topics include stationarity, autocovariance function and spectrum; integral representation of a stationary time series and interpretation; linear filtering, transfer functions; estimation of spectrum; multivariate time series. Use of computer programs for covariance and spectral estimation.
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Semester: Spring
Prerequisites: STAT 516 and 511. Knowledge of some computer language or statistical package.
Credits: 3
Primary Audience:
Description: A first course in stationary time series with applications using real and simulated data. Computing projects are assigned, so some computer language (e.g. C, FORTRAN, SPLUS etc.) or statistical package should also be familiar. Topics include stationarity, autocovariance function and spectrum; integral representation of a stationary time series and interpretation; linear filtering, transfer functions; estimation of spectrum; multivariate time series. Use of computer programs for covariance and spectral estimation.
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STAT 522 Sampling and Survey Techniques (Banner Course Number: 52200)
Semester: Spring
Prerequisites: STAT 512 or 517.
Credits: 3
Primary Audience:
Description: This course is taught every other Spring. A survey of sampling design and analysis of sample survey data, with emphasis on properties of estimates based on complex samples. Topics include survey designs; simple random, stratified, and systematic samples; systems of sampling; methods of estimation; costs.
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Semester: Spring
Prerequisites: STAT 512 or 517.
Credits: 3
Primary Audience:
Description: This course is taught every other Spring. A survey of sampling design and analysis of sample survey data, with emphasis on properties of estimates based on complex samples. Topics include survey designs; simple random, stratified, and systematic samples; systems of sampling; methods of estimation; costs.
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STAT 524 Applied Multivariate Analysis (Banner Course Number: 52400)
Semester: Fall
Prerequisites: STAT 512 or equivalent, or consent of instructor.
Credits: 3
Primary Audience:
Description: A detailed exposition of some of the more commonly used multivariate statistical techniques, including the geometric intuition underlying their use. Familiarity with the notation and the basic operations of matrix algebra, and with the standard univariate statistical procedures (in the depth to be found in STAT 512) is assumed. Some experience with SAS is highly recommended. Topics include dimension reduction techniques (principal components, factor analysis, and canonical correlation), clustering, classification, neural network, and structural equation models.
The software used is SAS whenever there is a SAS proc available for the topic. R will be used for bagging and support vector machine.
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Semester: Fall
Prerequisites: STAT 512 or equivalent, or consent of instructor.
Credits: 3
Primary Audience:
Description: A detailed exposition of some of the more commonly used multivariate statistical techniques, including the geometric intuition underlying their use. Familiarity with the notation and the basic operations of matrix algebra, and with the standard univariate statistical procedures (in the depth to be found in STAT 512) is assumed. Some experience with SAS is highly recommended. Topics include dimension reduction techniques (principal components, factor analysis, and canonical correlation), clustering, classification, neural network, and structural equation models.
The software used is SAS whenever there is a SAS proc available for the topic. R will be used for bagging and support vector machine.
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STAT 525 Intermediate Statistical Methodology (Banner Course Number: 52500)
Semester: Fall Spring
Prerequisites: STAT 517
Credits: 3
Primary Audience:
Description: Intended primarily to introduce statistics graduate students to data analysis, this course is open to other students who have the STAT 528 corequisite. Covers a large number of standard applied techniques with emphasis on the interplay of models and data. Statistical computing with SAS is stressed. Topics include likelihood methods for analyzing data based on generalized linear models, and diagnostic methods for assessing the assumptions of such models. Introduction to statistical computer packages. Methods covered include multiple regression, analysis of variance for completely randomized designs, logistic response models, and hierarchical log linear models for contingency tables.
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Semester: Fall Spring
Prerequisites: STAT 517
Credits: 3
Primary Audience:
Description: Intended primarily to introduce statistics graduate students to data analysis, this course is open to other students who have the STAT 528 corequisite. Covers a large number of standard applied techniques with emphasis on the interplay of models and data. Statistical computing with SAS is stressed. Topics include likelihood methods for analyzing data based on generalized linear models, and diagnostic methods for assessing the assumptions of such models. Introduction to statistical computer packages. Methods covered include multiple regression, analysis of variance for completely randomized designs, logistic response models, and hierarchical log linear models for contingency tables.
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STAT 526 Advanced Statistical Methodology (Banner Course Number: 52600)
Semester: Fall Spring
Prerequisites: STAT 525 and 517 or 528.
Credits: 3
Primary Audience:
Description: Taught Fall 2005 and every Spring. Computationally intensive methods in statistics including bootstrapping, Monte Carlo simulation, nonparametric density estimation, nonparametric regression and methods appropriate for high-dimensional data. Extensive use is made of statistical software.
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Semester: Fall Spring
Prerequisites: STAT 525 and 517 or 528.
Credits: 3
Primary Audience:
Description: Taught Fall 2005 and every Spring. Computationally intensive methods in statistics including bootstrapping, Monte Carlo simulation, nonparametric density estimation, nonparametric regression and methods appropriate for high-dimensional data. Extensive use is made of statistical software.
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STAT 528 Introduction to Mathematical Statistics (Banner Course Number: 52800)
Semester: Fall Spring
Prerequisites: STAT 519.
Credits: 3
Primary Audience:
Description: Distribution of mean and s2 in normal samples, sampling distributions derived from the normal distribution Chi square, t and F. Distribution of statistics based on ordered samples. Asymptotic sampling distributions. Introduction to multivariate normal distribution and linear models. Sufficient statistics, maximum likelihood, least squares, linear estimation, other methods of point estimation, and discussion of their properties, Cramer-Rao inequality and Rao-Blackwell theorem. Tests of statistical hypotheses, simple and composite hypotheses, likelihood ratio tests, power of tests.
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Semester: Fall Spring
Prerequisites: STAT 519.
Credits: 3
Primary Audience:
Description: Distribution of mean and s2 in normal samples, sampling distributions derived from the normal distribution Chi square, t and F. Distribution of statistics based on ordered samples. Asymptotic sampling distributions. Introduction to multivariate normal distribution and linear models. Sufficient statistics, maximum likelihood, least squares, linear estimation, other methods of point estimation, and discussion of their properties, Cramer-Rao inequality and Rao-Blackwell theorem. Tests of statistical hypotheses, simple and composite hypotheses, likelihood ratio tests, power of tests.
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STAT 529K Bayesian Applied Decision Theory (Banner Course Number: 52900)
Semester: Summer
Prerequisites: STAT 517 or equivalent
Credits: 3
Primary Audience: Applied Statisticians and other disciplines who use data to make decisions.
Description: Offered Maymester. The Bayesian Decision Theoretic Model, various loss (utility) functions and practical problems. Admissibility, minimax procedures. Selecting the prior and computations for the posterior. Hierarchical Bayesian and empirical Bayesian models, Markov Chain, Monte Carlo (MCMC) techniques. Robust Bayesian methods; sequential Bayesian models. Throughout the course practical examples will be introduced with the emphasis on understanding how to apply the theoretical concepts.
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Semester: Summer
Prerequisites: STAT 517 or equivalent
Credits: 3
Primary Audience: Applied Statisticians and other disciplines who use data to make decisions.
Description: Offered Maymester. The Bayesian Decision Theoretic Model, various loss (utility) functions and practical problems. Admissibility, minimax procedures. Selecting the prior and computations for the posterior. Hierarchical Bayesian and empirical Bayesian models, Markov Chain, Monte Carlo (MCMC) techniques. Robust Bayesian methods; sequential Bayesian models. Throughout the course practical examples will be introduced with the emphasis on understanding how to apply the theoretical concepts.
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STAT 532 Elements of Stochastic Processes (MA 532) (Banner Course Number: 53200)
Semester: Spring
Prerequisites: STAT 519.
Credits: 3
Primary Audience:
Description: A basic course in stochastic models, including discrete and continuous time Markov chains and Brownian motion, as well as an introduction to topics such as Gaussian processes, queues, epidemic models, branching processes, renewal processes, replacement, and reliability problems.
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Semester: Spring
Prerequisites: STAT 519.
Credits: 3
Primary Audience:
Description: A basic course in stochastic models, including discrete and continuous time Markov chains and Brownian motion, as well as an introduction to topics such as Gaussian processes, queues, epidemic models, branching processes, renewal processes, replacement, and reliability problems.
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STAT 538 Probability Theory I (MA 538) (Banner Course Number: 53800)
Semester: Spring
Prerequisites: MA 504 or equivalent.
Credits: 3
Primary Audience:
Description: Mathematically rigorous, measure-theoretic introduction to probability spaces, random variables, independence, weak and strong laws of large numbers, conditional expectations and martingales.
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Semester: Spring
Prerequisites: MA 504 or equivalent.
Credits: 3
Primary Audience:
Description: Mathematically rigorous, measure-theoretic introduction to probability spaces, random variables, independence, weak and strong laws of large numbers, conditional expectations and martingales.
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STAT 539 Probability Theory II (MA 539) (Banner Course Number: 53900)
Semester: Fall
Prerequisites: STAT 538 and MA 530.
Credits: 3
Primary Audience:
Description: Convergence of probability laws; characteristic functions; convergence to the normal law; infinitely divisible and stable laws; Brownian motion and the invariance principle.
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Semester: Fall
Prerequisites: STAT 538 and MA 530.
Credits: 3
Primary Audience:
Description: Convergence of probability laws; characteristic functions; convergence to the normal law; infinitely divisible and stable laws; Brownian motion and the invariance principle.
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STAT 540 Mathematics of Finance (MA 515, Before 2005 listed as STAT 598F) (Banner Course Number: 54000)
Semester: Spring
Prerequisites: A graduate introduction to probability theory (no measure theory needed): MA 519 (or equivalent) strongly desirable; otherwise, concurrent enrollment required. Multivariate calculus: MA 261 (or equivalent) required; a higher course desirable. Real analysis: MA 440 (or equivalent) required. MA 504 desirable. Differential equations: MA 360 or 364 or 366 (or equivalent) required.
Credits: 3
Primary Audience: MS Students in Statistics, Mathematics and Management who are working towards the Computational Finance Specialization
Description: Before 2005, listed as STAT 598F. Also taught as MA 515. We will provide an introduction to the mathematical tools and techniques of modern finance theory, in the context of Black-Scholes-style option pricing. The typical (pricing) question is: how much should you charge someone for allowing them the right to purchase a certain stock from you at a given price and a given time in the future? Once a price has been determined, the most important question is that of hedging: how can you ensure that the price you charge for the option is invested in order to cover your risk no matter what happens to the future stock movements. The typical (Black-Scholes) assumption is that the relative differential of the stock price is proportional to the sum of a constant term (constant interest rate) and a random noise term. Under this assumption, to answer the pricing question, the main mathematical tool is stochastic calculus and its connection to partial differential equations. These mathematics will be the object of a thorough introduction at an elementary level, without measure theory. This toolbox will enable us to derive the main pricing and hedging results, and to treat many examples and topics including incomplete markets, path-dependent options, and other exotic options. Towards the end of the semester, we will cover a more difficult topic, that of stochastic portlio optimization: how do you maximize the expected future return of a portfolio using the Black-Scholes model. The main tool here will be stochastic control theory and its associated Hamilton-Jacobi-Bellman equations.
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Semester: Spring
Prerequisites: A graduate introduction to probability theory (no measure theory needed): MA 519 (or equivalent) strongly desirable; otherwise, concurrent enrollment required. Multivariate calculus: MA 261 (or equivalent) required; a higher course desirable. Real analysis: MA 440 (or equivalent) required. MA 504 desirable. Differential equations: MA 360 or 364 or 366 (or equivalent) required.
Credits: 3
Primary Audience: MS Students in Statistics, Mathematics and Management who are working towards the Computational Finance Specialization
Description: Before 2005, listed as STAT 598F. Also taught as MA 515. We will provide an introduction to the mathematical tools and techniques of modern finance theory, in the context of Black-Scholes-style option pricing. The typical (pricing) question is: how much should you charge someone for allowing them the right to purchase a certain stock from you at a given price and a given time in the future? Once a price has been determined, the most important question is that of hedging: how can you ensure that the price you charge for the option is invested in order to cover your risk no matter what happens to the future stock movements. The typical (Black-Scholes) assumption is that the relative differential of the stock price is proportional to the sum of a constant term (constant interest rate) and a random noise term. Under this assumption, to answer the pricing question, the main mathematical tool is stochastic calculus and its connection to partial differential equations. These mathematics will be the object of a thorough introduction at an elementary level, without measure theory. This toolbox will enable us to derive the main pricing and hedging results, and to treat many examples and topics including incomplete markets, path-dependent options, and other exotic options. Towards the end of the semester, we will cover a more difficult topic, that of stochastic portlio optimization: how do you maximize the expected future return of a portfolio using the Black-Scholes model. The main tool here will be stochastic control theory and its associated Hamilton-Jacobi-Bellman equations.
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STAT 541 Advanced Probability and Options, with Numerical Methods (MA 516, Before 2005 listed as STAT 598G) (Banner Course Number: 54100)
Semester: Fall
Prerequisites: STAT 598F or STAT 540
Credits: 3
Primary Audience: M.S. Students in Statistics, Mathematics and Management who are working towards the Computational Finance Specialization
Description: Before 2005, listed as STAT 598G. Also taught as MA 516. This is the second course in a two-course sequence on the mathematics of finance, and especially on option pricing. The material is divided into two parts. First, the course covers theoretical issues regarding: (i) interest rate term structure models; (ii) American options and stochastic optimal stopping; (iii) finite difference methods. Then the course examines in detail the numerical methods used to solve the partial differential equations and inequalities that determine the prices of options, including the Binomial, Trinomial, Monte-Carlo, and finite difference methods.
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Semester: Fall
Prerequisites: STAT 598F or STAT 540
Credits: 3
Primary Audience: M.S. Students in Statistics, Mathematics and Management who are working towards the Computational Finance Specialization
Description: Before 2005, listed as STAT 598G. Also taught as MA 516. This is the second course in a two-course sequence on the mathematics of finance, and especially on option pricing. The material is divided into two parts. First, the course covers theoretical issues regarding: (i) interest rate term structure models; (ii) American options and stochastic optimal stopping; (iii) finite difference methods. Then the course examines in detail the numerical methods used to solve the partial differential equations and inequalities that determine the prices of options, including the Binomial, Trinomial, Monte-Carlo, and finite difference methods.
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STAT 549 An Applied Intro. to QTL Mapping in Experimental Populations (Banner Course Number: 54900)
Semester: Spring
Prerequisites: STAT 511 and STAT 512
Credits: 3
Primary Audience: Interdisciplinary
Description: The detection of genes that control quantitative characters is a problem of great interest to many areas of research including bioinformatics, genomics, statistical genetics, computer science, mathematics, horticulture, biology, agronomy, genetics, and plant breeding. In this introductory course, basic experimental breeding designs (e.g., backcross, F2, and RI) will be used to investigate estimation of recombination fractions or genetic distances between molecular markers and/or quantitative trait loci (QTL). Standard genetic mapping techniques will be presented for the purpose of estimating genetic maps and employing current software (MAPMAKER/EXP, Map Manager QT ) for map building. Both simulated and real data will be used to demonstrate the map building process.
Once a genetic map is in place, this structure is used to search for associations with quantitative traits, and then to locate regions in the genome known as quantitative trait loci (QTL). MAPMAKER/QTL, QTL-Cartographer, and Map Manager QT will be introduced and utilized throughout this course for the purpose of understanding how the algorithms are designed and employed for proper QTL mapping. Mapping methodologies such as single marker QTL mapping, interval mapping, and composite interval mapping will be covered in detail. In addition, resampling techniques (i.e., bootstrapping, permutations) will be presented as methods for estimating critical values used to declare significant QTL.
An introduction to Affymetrix arrays and spotted microarrays will be provided for the purpose of setting the stage for mapping natural variation in the context of funtional genomics.
This is a graduate level interdisciplinary course that requires minimal computer experience, lower levels of statistical training (STAT 503 or 511, and/or STAT 512), and a maximal level of motivation. All students who are interested in genomics and/or bioinformatics are encouraged to come learn about QTL mapping. Students who have real data as part of their research program are especially encouraged to participate.
Software (all free, with manuals): MAPMAKER/EXP, MAPMAKER/QTL, QTL-Cartographer, and Map Manager QT (maybe).
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Semester: Spring
Prerequisites: STAT 511 and STAT 512
Credits: 3
Primary Audience: Interdisciplinary
Description: The detection of genes that control quantitative characters is a problem of great interest to many areas of research including bioinformatics, genomics, statistical genetics, computer science, mathematics, horticulture, biology, agronomy, genetics, and plant breeding. In this introductory course, basic experimental breeding designs (e.g., backcross, F2, and RI) will be used to investigate estimation of recombination fractions or genetic distances between molecular markers and/or quantitative trait loci (QTL). Standard genetic mapping techniques will be presented for the purpose of estimating genetic maps and employing current software (MAPMAKER/EXP, Map Manager QT ) for map building. Both simulated and real data will be used to demonstrate the map building process.
Once a genetic map is in place, this structure is used to search for associations with quantitative traits, and then to locate regions in the genome known as quantitative trait loci (QTL). MAPMAKER/QTL, QTL-Cartographer, and Map Manager QT will be introduced and utilized throughout this course for the purpose of understanding how the algorithms are designed and employed for proper QTL mapping. Mapping methodologies such as single marker QTL mapping, interval mapping, and composite interval mapping will be covered in detail. In addition, resampling techniques (i.e., bootstrapping, permutations) will be presented as methods for estimating critical values used to declare significant QTL.
An introduction to Affymetrix arrays and spotted microarrays will be provided for the purpose of setting the stage for mapping natural variation in the context of funtional genomics.
This is a graduate level interdisciplinary course that requires minimal computer experience, lower levels of statistical training (STAT 503 or 511, and/or STAT 512), and a maximal level of motivation. All students who are interested in genomics and/or bioinformatics are encouraged to come learn about QTL mapping. Students who have real data as part of their research program are especially encouraged to participate.
Software (all free, with manuals): MAPMAKER/EXP, MAPMAKER/QTL, QTL-Cartographer, and Map Manager QT (maybe).
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STAT 551 Applied Survival Analysis (Banner Course Number: 55100)
Semester: Spring
Prerequisites: STAT 512 or equivalent, STAT 517 or equivalent, or permission of instructor.
Credits: 3
Primary Audience: Graduate Students
Description: Analysis of survival data is a classical problem to applied statisticians involved in biomedical and economical studies. This course introduces students to the basic concepts, and special statistical tools in analyzing censored and/or truncated data. The survival analysis procedures in SAS system will be covered, along with selected topics in current research.
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Semester: Spring
Prerequisites: STAT 512 or equivalent, STAT 517 or equivalent, or permission of instructor.
Credits: 3
Primary Audience: Graduate Students
Description: Analysis of survival data is a classical problem to applied statisticians involved in biomedical and economical studies. This course introduces students to the basic concepts, and special statistical tools in analyzing censored and/or truncated data. The survival analysis procedures in SAS system will be covered, along with selected topics in current research.
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STAT 553 Theory of Linear Models and Analysis of Experimental Designs (Banner Course Number: 55300)
Semester: Spring
Prerequisites: STAT 528 and a firm background in matrix algebra. Some previous exposure to linear models or analysis of variance is desirable.
Credits: 3
Primary Audience:
Description: Least squares analysis of linear models. Gauss Markov Theorem. Estimability and testability of parameters. Confidence regions and prediction regions. Introduction to design of experiments. Analysis of variance. Factorial and block designs. Analysis of random, fixed and mixed models. Components of variance. Distribution of linear and quadratic forms in normal vectors.
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Semester: Spring
Prerequisites: STAT 528 and a firm background in matrix algebra. Some previous exposure to linear models or analysis of variance is desirable.
Credits: 3
Primary Audience:
Description: Least squares analysis of linear models. Gauss Markov Theorem. Estimability and testability of parameters. Confidence regions and prediction regions. Introduction to design of experiments. Analysis of variance. Factorial and block designs. Analysis of random, fixed and mixed models. Components of variance. Distribution of linear and quadratic forms in normal vectors.
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STAT 576 Statistical Decision Theory and Bayesian Analysis (Banner Course Number: 57600)
Semester: Spring
Prerequisites: MA 519 or equivalent; STAT 528 or equivalent.
Credits: 3
Primary Audience:
Description: Formulation of the general statistical decision problem; foundations, utility, and prior information. Bayesian analysis including inference, decision making, empirical and hierarchical Bayes, combination of evidence, and robustness. Introduction to game theory, minimax procedures, sufficiency, admissibility, and complete classes.
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Semester: Spring
Prerequisites: MA 519 or equivalent; STAT 528 or equivalent.
Credits: 3
Primary Audience:
Description: Formulation of the general statistical decision problem; foundations, utility, and prior information. Bayesian analysis including inference, decision making, empirical and hierarchical Bayes, combination of evidence, and robustness. Introduction to game theory, minimax procedures, sufficiency, admissibility, and complete classes.
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STAT 580 Application of Statistical Theory (Banner Course Number: 58000)
Semester: Fall Summer
Prerequisites: Corequisite: STAT 528, 539, 576, or equivalent, and some knowledge of complex analysis. STAT 532, STAT 554, and computer skills desirable.
Credits: 3
Primary Audience:
Description: The use of numerical methods to obtain answers in problems arising in probability and statistics. Topics will include the use of the likelihood function, Bayesian and classical methods of estimation and testing, evaluation of probabilities, linear and nonlinear regression.
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Semester: Fall Summer
Prerequisites: Corequisite: STAT 528, 539, 576, or equivalent, and some knowledge of complex analysis. STAT 532, STAT 554, and computer skills desirable.
Credits: 3
Primary Audience:
Description: The use of numerical methods to obtain answers in problems arising in probability and statistics. Topics will include the use of the likelihood function, Bayesian and classical methods of estimation and testing, evaluation of probabilities, linear and nonlinear regression.
Schedule and Textbook Information for Fall 2008
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STAT 582 Statistical Consulting and Collaboration (Banner Course Number: 58200)
Semester: Spring
Prerequisites: Consent of the Instructor
Credits: 3
Primary Audience: Graduates Students in Statistics
Description: This course was previously taught as STAT 598S.To emphasize and develop the skills needed by a statistical consultant/collaborator. Topics include problem solving, consulting session management, written and oral communication, research ethics, design of experiments, collection of data, and application of statistical methods to real problems. Class activities include actively participating in consulting sessions held by the Statistical Consulting Service, small group projects, short papers, and oral presentations. This course can be taken in place of STAT 515, which will meet in conjunction with this course one day a week.
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Semester: Spring
Prerequisites: Consent of the Instructor
Credits: 3
Primary Audience: Graduates Students in Statistics
Description: This course was previously taught as STAT 598S.To emphasize and develop the skills needed by a statistical consultant/collaborator. Topics include problem solving, consulting session management, written and oral communication, research ethics, design of experiments, collection of data, and application of statistical methods to real problems. Class activities include actively participating in consulting sessions held by the Statistical Consulting Service, small group projects, short papers, and oral presentations. This course can be taken in place of STAT 515, which will meet in conjunction with this course one day a week.
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STAT 590 Internship Seminar (Banner Course Number: 59000)
Semester: Fall Spring Summer
Prerequisites: Consent of instructor required. Satisfactory completion of at least one year of graduate study in statistics.
Credits: 3
Primary Audience:
Description: Students complete an internship where they will use statistical methods. A detailed report describing the internship work is required.
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Semester: Fall Spring Summer
Prerequisites: Consent of instructor required. Satisfactory completion of at least one year of graduate study in statistics.
Credits: 3
Primary Audience:
Description: Students complete an internship where they will use statistical methods. A detailed report describing the internship work is required.
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STAT 597 Statistical Consulting Seminar (Banner Course Number: 59700)
Semester: Fall Spring Summer
Prerequisites: STAT 514, STAT 525, and consent of instructor.
Credits: 1-2
Primary Audience: Graduate students in Statistics
Description: Active participation in weekly consulting meetings, directed reading in the statistical literature, application of statistical methods to real problems, report writing. This course may be taken several times for credit, but at most, three credits may count toward fulfilling the requirements for any degree.
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Semester: Fall Spring Summer
Prerequisites: STAT 514, STAT 525, and consent of instructor.
Credits: 1-2
Primary Audience: Graduate students in Statistics
Description: Active participation in weekly consulting meetings, directed reading in the statistical literature, application of statistical methods to real problems, report writing. This course may be taken several times for credit, but at most, three credits may count toward fulfilling the requirements for any degree.
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STAT 598 Topics in Statistical Methods (Banner Course Number: 59800)
Semester: Fall Spring Summer
Prerequisites:
Credits: 1-6
Primary Audience:
Description: Directed study and reports for students who wish to undertake individual reading and study on approved topics. (May be repeated for credit).
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Semester: Fall Spring Summer
Prerequisites:
Credits: 1-6
Primary Audience:
Description: Directed study and reports for students who wish to undertake individual reading and study on approved topics. (May be repeated for credit).
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STAT 598A Analysis of Massive Dependent Data
(Banner Course Number: 59800)
Semester: Fall
Prerequisites: STAT 516
Credits: 3
Primary Audience: Graduate students who have taken one course in probability and one course in statistics at the MS level or above
Description: Due to the technological innovation and improved ability to acquire and achieve data, huge amount of data are collected in many disciplines including environmental, agricultural and public health studies. For example, the EPA has thousands of monitoring stations across the US that measure air quality, ozone level and other variables; Ecologists insert censors to animals to track the animals and these censors reveal the animal?s whereabouts at any moments; Hospitals record the admission of patients having various diseases to monitor the disease outbreak. These data have a space attribute (where are observed) and also a time dimension (when they are observed). These kinds of correlated data are more prevalent these days than anytime before, and present some interesting and challenging statistical and computing problems.
If these data were independent, the challenges would be reduced significantly. This course covers methods that deal with the additional complexity of modeling and analyzing the massive data, which is attributed to the correlation or dependence in space and time. Some topics include sparse matrices, approximate likelihood-based inferences, covariance tapering, spectral methods, separable space-time covariance functions, and process convolution.
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Semester: Fall
Prerequisites: STAT 516
Credits: 3
Primary Audience: Graduate students who have taken one course in probability and one course in statistics at the MS level or above
Description: Due to the technological innovation and improved ability to acquire and achieve data, huge amount of data are collected in many disciplines including environmental, agricultural and public health studies. For example, the EPA has thousands of monitoring stations across the US that measure air quality, ozone level and other variables; Ecologists insert censors to animals to track the animals and these censors reveal the animal?s whereabouts at any moments; Hospitals record the admission of patients having various diseases to monitor the disease outbreak. These data have a space attribute (where are observed) and also a time dimension (when they are observed). These kinds of correlated data are more prevalent these days than anytime before, and present some interesting and challenging statistical and computing problems.
If these data were independent, the challenges would be reduced significantly. This course covers methods that deal with the additional complexity of modeling and analyzing the massive data, which is attributed to the correlation or dependence in space and time. Some topics include sparse matrices, approximate likelihood-based inferences, covariance tapering, spectral methods, separable space-time covariance functions, and process convolution.
Schedule and Textbook Information for Fall 2008
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STAT 598B Bioinformatics/Statistical Genomics Seminar (Banner Course Number: 59800)
Semester: Fall Spring
Prerequisites:
Credits: 1
Primary Audience:
Description: Meets the same time and place as the Bioinformatics/Statistical Genomics Seminar.
Schedule and Textbook Information for Fall 2008
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Semester: Fall Spring
Prerequisites:
Credits: 1
Primary Audience:
Description: Meets the same time and place as the Bioinformatics/Statistical Genomics Seminar.
Schedule and Textbook Information for Fall 2008
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STAT 598C Statistical Methods for Bioinformatics and Computational Biology (Banner Course Number: 59800)
Semester: Fall
Prerequisites: At least one course from the list of STAT 514, STAT 524 and STAT 525, and experience with R. A knowledge of basic biological concepts is desirable, but not required
Credits: 3
Primary Audience: Graduate students (both MS and PhD) from statistics and life sciences
Description: The course discusses statistical methods and algorithms for analysis of high-throughput experiments in molecular biology. First, it introduces relevant biological concepts, and describes the existing high-throughput technologies and biological questions that these technologies can help answer.
Second, using gene expression microarrays as example, the course discusses statistical methods used at various stages of analysis. It covers statistical concepts and models, as well as data structures and implementation of the methods in the R-based open source project Bioconductor.
The course is project-driven and provides hands-on experience with data analysis, critical review of literature, and communication of the results. At the end of the course the students will be able to perform independent analysis of biological data in an interdisciplinary environment such as a pharmaceutical company, or a computational biology research lab.
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Semester: Fall
Prerequisites: At least one course from the list of STAT 514, STAT 524 and STAT 525, and experience with R. A knowledge of basic biological concepts is desirable, but not required
Credits: 3
Primary Audience: Graduate students (both MS and PhD) from statistics and life sciences
Description: The course discusses statistical methods and algorithms for analysis of high-throughput experiments in molecular biology. First, it introduces relevant biological concepts, and describes the existing high-throughput technologies and biological questions that these technologies can help answer.
Second, using gene expression microarrays as example, the course discusses statistical methods used at various stages of analysis. It covers statistical concepts and models, as well as data structures and implementation of the methods in the R-based open source project Bioconductor.
The course is project-driven and provides hands-on experience with data analysis, critical review of literature, and communication of the results. At the end of the course the students will be able to perform independent analysis of biological data in an interdisciplinary environment such as a pharmaceutical company, or a computational biology research lab.
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STAT 598D.SP08 Computational Statistics (Banner Course Number: 59800)
Semester: Spring
Prerequisites: STAT598G
Credits: 3
Primary Audience: Statistics Graduate Students
Description: The course focuses on two fundamental aspects in computational statistics: (1) what to compute and (2) how to compute. The first is covered with a brief review of advanced topics in statistical inference, including Fisher's fiducial inference, Bayesian and frequenstist methods, and the Dempster-Shafer (DS) theory. The second is discussed in detail by examining exact, approximation, and iterative simulation methods for statistical inference with a variety of commonly used statistical models. The emphasis is on the EM-type and quasi-Newton algorithms, numerical differentiation and integration, and Markov chain Monte Carlo methods.
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Semester: Spring
Prerequisites: STAT598G
Credits: 3
Primary Audience: Statistics Graduate Students
Description: The course focuses on two fundamental aspects in computational statistics: (1) what to compute and (2) how to compute. The first is covered with a brief review of advanced topics in statistical inference, including Fisher's fiducial inference, Bayesian and frequenstist methods, and the Dempster-Shafer (DS) theory. The second is discussed in detail by examining exact, approximation, and iterative simulation methods for statistical inference with a variety of commonly used statistical models. The emphasis is on the EM-type and quasi-Newton algorithms, numerical differentiation and integration, and Markov chain Monte Carlo methods.
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STAT 598E Qualifier Preparation (Banner Course Number: 59800)
Semester: Fall
Prerequisites: Consent of Professor T. Sellke is Required.
Credits: 3
Primary Audience: Restricted to graduate students in the Statistics Ph.D. Program
Description: This course provides review for the Statistics Ph.D. qualifying exams.
Schedule and Textbook Information for Fall 2008
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Semester: Fall
Prerequisites: Consent of Professor T. Sellke is Required.
Credits: 3
Primary Audience: Restricted to graduate students in the Statistics Ph.D. Program
Description: This course provides review for the Statistics Ph.D. qualifying exams.
Schedule and Textbook Information for Fall 2008
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STAT 598F.SUM08 Qualifier Prep Course (Banner Course Number: 59800)
Semester: Summer
Prerequisites: Consent of Professor T. Sellke is required.
Credits: 3
Primary Audience: Restricted to graduate students in the Statistics Ph.D. Program.
Description: This course provides review for the Statistics Ph.D. qualifying exams.
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Semester: Summer
Prerequisites: Consent of Professor T. Sellke is required.
Credits: 3
Primary Audience: Restricted to graduate students in the Statistics Ph.D. Program.
Description: This course provides review for the Statistics Ph.D. qualifying exams.
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STAT 598G Introduction to Computational Statistics (Banner Course Number: 59800)
Semester: Fall Spring
Prerequisites: Instructor Permission. STAT 516, STAT 517, and some familiarity with computing. In particular, the students should have some programming experience using a language such as C, C++, Pascal, FORTRAN, Java (the students should be able to write, debug and compile a simple program in one of the above languages).
Credits: 3
Primary Audience: Statistics Graduate Students
Description: This introductory course covers the fundamentals of computing for statistics and data analysis. It starts with a brief overview of programming using a general purpose compiled language (C) and a statistics-oriented interpreted language (R). The course proceeds to cover data structures and algorithms that are directly relevant to statistics and data analysis and concludes with a computing-oriented introduction to selected statistical methods. A significant part of the course involves programming and hands-on experimentation demonstrating the covered techniques.
Schedule and Textbook Information for Fall 2008
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Course Page for Fall 2008
Semester: Fall Spring
Prerequisites: Instructor Permission. STAT 516, STAT 517, and some familiarity with computing. In particular, the students should have some programming experience using a language such as C, C++, Pascal, FORTRAN, Java (the students should be able to write, debug and compile a simple program in one of the above languages).
Credits: 3
Primary Audience: Statistics Graduate Students
Description: This introductory course covers the fundamentals of computing for statistics and data analysis. It starts with a brief overview of programming using a general purpose compiled language (C) and a statistics-oriented interpreted language (R). The course proceeds to cover data structures and algorithms that are directly relevant to statistics and data analysis and concludes with a computing-oriented introduction to selected statistical methods. A significant part of the course involves programming and hands-on experimentation demonstrating the covered techniques.
Schedule and Textbook Information for Fall 2008
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STAT 598I.SP08 Computational Finance Seminar (Banner Course Number: 59800)
Semester: Spring
Prerequisites: None.
Credits: 1
Primary Audience: 1. MS or MBA students enrolled in the Computational Finance Program and 2. PhD in Statistics/Mathematics interested in Mathematical/Computational Finance Issues
Description: The weekly Computational Finance Seminar brings in academic and industrial leaders from the financial world, providing valuable contacts for students as well as exposure to current research problems in mathematical finance. The topics discussed at the seminar include numerical methods for option pricing, calibration and computational methods for fixed income instruments, local volatility models, calibration of jump-diffusion models, portfolio management, and risk management. It fosters exchanges among faculty and students from different departments at Purdue and also from different Universities in the area. The seminar, together with the Core Computational Finance courses, creates a strong sense of CF community at Purdue.
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Semester: Spring
Prerequisites: None.
Credits: 1
Primary Audience: 1. MS or MBA students enrolled in the Computational Finance Program and 2. PhD in Statistics/Mathematics interested in Mathematical/Computational Finance Issues
Description: The weekly Computational Finance Seminar brings in academic and industrial leaders from the financial world, providing valuable contacts for students as well as exposure to current research problems in mathematical finance. The topics discussed at the seminar include numerical methods for option pricing, calibration and computational methods for fixed income instruments, local volatility models, calibration of jump-diffusion models, portfolio management, and risk management. It fosters exchanges among faculty and students from different departments at Purdue and also from different Universities in the area. The seminar, together with the Core Computational Finance courses, creates a strong sense of CF community at Purdue.
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STAT 598M.F07 Data Mining (CS 590D) (Banner Course Number: 59800)
Semester: Fall
Prerequisites: STAT 511 or equivalent, CS 381 or equivalent, or permission of instructor.
Credits: 3
Primary Audience: Graduate Students
Description: Data Mining has emerged at the confluence of artificial intelligence, statistics, and databases as a technique for automatically discovering summary knowledge in large datasets. This course introduces students to the process and main techniques in data mining, including classification, clustering, and pattern mining approaches. Data mining systems and applications will also be covered, along with selected topics in current research.
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Semester: Fall
Prerequisites: STAT 511 or equivalent, CS 381 or equivalent, or permission of instructor.
Credits: 3
Primary Audience: Graduate Students
Description: Data Mining has emerged at the confluence of artificial intelligence, statistics, and databases as a technique for automatically discovering summary knowledge in large datasets. This course introduces students to the process and main techniques in data mining, including classification, clustering, and pattern mining approaches. Data mining systems and applications will also be covered, along with selected topics in current research.
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STAT 598M.SP08 Wavelets and Multiresolution Analysis (Banner Course Number: 59800)
Semester:
Prerequisites: Mathematical maturity and a solid background in calculus.
Credits: 1
Primary Audience: Graduate students at the MS or Ph.D. level in Statistics, CS, ECE, Mathematics or other similar disciplines.
Description: The course will cover the fundamentals of wavelets and multiresolution analysis with an emphasis on their applications to statistics and machine learning. We will start by reviewing Fourier analysis and then proceed to present various topics related to wavelets. We will follow some of the sections in the textbook "A Wavelet Tour of Signal Processing" by S. Mallat (2nd edition) as well as some papers describing machine learning and statistics applications. The course will have a substantial mathematical component but there is no official prerequisite beyond mathematical maturity and a solid background in calculus.
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Semester:
Prerequisites: Mathematical maturity and a solid background in calculus.
Credits: 1
Primary Audience: Graduate students at the MS or Ph.D. level in Statistics, CS, ECE, Mathematics or other similar disciplines.
Description: The course will cover the fundamentals of wavelets and multiresolution analysis with an emphasis on their applications to statistics and machine learning. We will start by reviewing Fourier analysis and then proceed to present various topics related to wavelets. We will follow some of the sections in the textbook "A Wavelet Tour of Signal Processing" by S. Mallat (2nd edition) as well as some papers describing machine learning and statistics applications. The course will have a substantial mathematical component but there is no official prerequisite beyond mathematical maturity and a solid background in calculus.
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STAT 598Q.SP07 Qualifier Preparation (Banner Course Number: 59800)
Semester: Fall Spring
Prerequisites: Consent of Professor T. Sellke is required.
Credits: 3
Primary Audience: Restricted to graduate students in the Statistics Ph.D. Program.
Description: This course provides review for the Statistics Ph.D. qualifying exams.
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Semester: Fall Spring
Prerequisites: Consent of Professor T. Sellke is required.
Credits: 3
Primary Audience: Restricted to graduate students in the Statistics Ph.D. Program.
Description: This course provides review for the Statistics Ph.D. qualifying exams.
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STAT 598R.SP09 Statistical Methods for Association Mapping (Banner Course Number: 59800)
Semester: Spring
Prerequisites: Introductory Statistics Course ( e.g., STAT 511, 512, 525), or permission of instructor
Credits: 3
Primary Audience: Statistics Graduate Students
Description: This course will be offered in Spring 2009. With the availability of high-throughput genotyping technologies and enormous polymorphisms, association mapping has been actively employed to identify susceptible genetic variants. While each of such studies produces massive amount of data, special statistical tools have been developed to preprocess the data, check the underlying assumptions and locate genes of interest. This course will introduce the basics of association mapping and classical statistical methods used for population association studies. The most recent literature will also be discussed.
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Semester: Spring
Prerequisites: Introductory Statistics Course ( e.g., STAT 511, 512, 525), or permission of instructor
Credits: 3
Primary Audience: Statistics Graduate Students
Description: This course will be offered in Spring 2009. With the availability of high-throughput genotyping technologies and enormous polymorphisms, association mapping has been actively employed to identify susceptible genetic variants. While each of such studies produces massive amount of data, special statistical tools have been developed to preprocess the data, check the underlying assumptions and locate genes of interest. This course will introduce the basics of association mapping and classical statistical methods used for population association studies. The most recent literature will also be discussed.
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STAT 598S Statistical Consulting and Collaboration (Banner Course Number: 59800)
Semester: Spring
Prerequisites: Consent of the Instructor
Credits: 3
Primary Audience: Graduates Students in Statistics
Description: This course will be taught as STAT 582 in Spring 2009. To emphasize and develop the skills needed by a statistical consultant/collaborator. Topics include problem solving, consulting session management, written and oral communication, research ethics, design of experiments, collection of data, and application of statistical methods to real problems. Class activities include actively participating in consulting sessions held by the Statistical Consulting Service, small group projects, short papers, and oral presentations. This course can be taken in place of STAT 515, which will meet in conjunction with this course one day a week.
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Semester: Spring
Prerequisites: Consent of the Instructor
Credits: 3
Primary Audience: Graduates Students in Statistics
Description: This course will be taught as STAT 582 in Spring 2009. To emphasize and develop the skills needed by a statistical consultant/collaborator. Topics include problem solving, consulting session management, written and oral communication, research ethics, design of experiments, collection of data, and application of statistical methods to real problems. Class activities include actively participating in consulting sessions held by the Statistical Consulting Service, small group projects, short papers, and oral presentations. This course can be taken in place of STAT 515, which will meet in conjunction with this course one day a week.
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STAT 598T.SP08 Statistical Network Analysis (Banner Course Number: 59800)
Semester: Spring
Prerequisites: Introductory Statistics Course (e.g., 416/516).
Credits: 3
Primary Audience: Graduate or upper-level undergraduate.
Description: Many modern data analysis problems involve large data sets of artificial, social, and biological networks that can be represented as graphs. In these settings, traditional IID assumptions are inappropriate; the analyses must take into account the structure of relationships between the data instances. As a result, there has been increasing amount of research developing techniques for incorporating network and graph structures into machine learning and statistics.
Network modeling is an active area of research in several domains. Statisticians have mostly concentrated on models of static networks, which focus on predicting the existence of edges between individual nodes, and do not attempt to model aggregate properties of the graph. In contrast, physicists have developed techniques to model global properties of large complex networks. Their models describe average statistics of the network and focus less on the individual links between particular nodes.
This course will provide an introduction to probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference. We will survey statistical network modeling research in multiple communities, including statistics, computer science, and physics.
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Semester: Spring
Prerequisites: Introductory Statistics Course (e.g., 416/516).
Credits: 3
Primary Audience: Graduate or upper-level undergraduate.
Description: Many modern data analysis problems involve large data sets of artificial, social, and biological networks that can be represented as graphs. In these settings, traditional IID assumptions are inappropriate; the analyses must take into account the structure of relationships between the data instances. As a result, there has been increasing amount of research developing techniques for incorporating network and graph structures into machine learning and statistics.
Network modeling is an active area of research in several domains. Statisticians have mostly concentrated on models of static networks, which focus on predicting the existence of edges between individual nodes, and do not attempt to model aggregate properties of the graph. In contrast, physicists have developed techniques to model global properties of large complex networks. Their models describe average statistics of the network and focus less on the individual links between particular nodes.
This course will provide an introduction to probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference. We will survey statistical network modeling research in multiple communities, including statistics, computer science, and physics.
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STAT 598U Statistics of Extremes (Banner Course Number: 59800)
Semester: Fall
Prerequisites: Introductory statistics course (STAT 511, STAT 517, or EAS 591S)
Credits: 3
Primary Audience: MS and PhD students in statistics, atmospheric sciences, engineering, and finance
Description: The emerging interdisciplinary science of extreme events attracts considerable attention of statisticians, engineers, scientists, and in the financial world. It has a rich statistical theory of extremes (very different from that for averages), while its rapid development is spurred by engineering practice, economic and environmental disasters, and global climate change with anticipated increases in the frequency and intensity of extreme events. The course will provide a systematic introduction to the extreme value theory as well as hands-on experience with real data.
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Semester: Fall
Prerequisites: Introductory statistics course (STAT 511, STAT 517, or EAS 591S)
Credits: 3
Primary Audience: MS and PhD students in statistics, atmospheric sciences, engineering, and finance
Description: The emerging interdisciplinary science of extreme events attracts considerable attention of statisticians, engineers, scientists, and in the financial world. It has a rich statistical theory of extremes (very different from that for averages), while its rapid development is spurred by engineering practice, economic and environmental disasters, and global climate change with anticipated increases in the frequency and intensity of extreme events. The course will provide a systematic introduction to the extreme value theory as well as hands-on experience with real data.
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STAT 598V Exploring Statistical Sciences Research (Banner Course Number: 59800)
Semester: Fall
Prerequisites:
Credits: 1
Primary Audience:
Description: Meets at same time and place as Exploring Statistical Sciences Seminar.
Schedule and Textbook Information for Fall 2008
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Semester: Fall
Prerequisites:
Credits: 1
Primary Audience:
Description: Meets at same time and place as Exploring Statistical Sciences Seminar.
Schedule and Textbook Information for Fall 2008
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STAT 598W.SP08 Design and Analysis of Financial Algorithms (Banner Course Number: 59800)
Semester: Spring
Prerequisites: MA 516/STAT 541.
Credits: 3
Primary Audience:
Description: Information technology (IT) has become a major function in the financial industry. The industry has been employing various software and programming languages to process and maintain the data, to price equity and fixed income derivatives, and to predict stock and index movements. In this course, students will learn basics of Excel VBA, MATLAB, C/C++, C#, and X_TRADER API, which are some of most useful programming tools in financial firms. They will apply their skills to coding and analyzing modern financial algorithms for pricing, hedging, or portfolio optimization.
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Semester: Spring
Prerequisites: MA 516/STAT 541.
Credits: 3
Primary Audience:
Description: Information technology (IT) has become a major function in the financial industry. The industry has been employing various software and programming languages to process and maintain the data, to price equity and fixed income derivatives, and to predict stock and index movements. In this course, students will learn basics of Excel VBA, MATLAB, C/C++, C#, and X_TRADER API, which are some of most useful programming tools in financial firms. They will apply their skills to coding and analyzing modern financial algorithms for pricing, hedging, or portfolio optimization.
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STAT 598Z.SP08 Applied Spatial Statistics (Banner Course Number: 59800)
Semester: Spring
Prerequisites: A graduate course in statistics or probability.
Credits: 3
Primary Audience: Students who are interested in analyzing spatial data.
Description: This course covers a wide range of statistical models and methods for data that are collected at different spatial locations and perhaps at different times. These data are called spatial or spatio-temporal data, which arise in many scientific disciplines such as agronomy, plant pathology, forestry and natural resources, environmental and health studies, climatology, geology, biosecurity, etc. Spatial statistics is currently one of the most active research area in statistics and there has been tremendous advancement in methodological and computational research in spatial statistics that enables us to analyze massive spatial data today. This course will introduce the classical methods as well as some newly developed ones, and will provide ample hands-on activities. The programming language R and a few packages for analyzing spatial data will be introduced. One objective is for students to be able to identify appropriate methods and analyze spatial data in their research.
Topics: This course covers statistical methods for georeferenced data (such as ozone measurements from different monitoring stations), spatial point patterns (such as incidents of plant/human disease), and areal data (such as county statistics in the US). An incomplete list of topics is as follows.
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Semester: Spring
Prerequisites: A graduate course in statistics or probability.
Credits: 3
Primary Audience: Students who are interested in analyzing spatial data.
Description: This course covers a wide range of statistical models and methods for data that are collected at different spatial locations and perhaps at different times. These data are called spatial or spatio-temporal data, which arise in many scientific disciplines such as agronomy, plant pathology, forestry and natural resources, environmental and health studies, climatology, geology, biosecurity, etc. Spatial statistics is currently one of the most active research area in statistics and there has been tremendous advancement in methodological and computational research in spatial statistics that enables us to analyze massive spatial data today. This course will introduce the classical methods as well as some newly developed ones, and will provide ample hands-on activities. The programming language R and a few packages for analyzing spatial data will be introduced. One objective is for students to be able to identify appropriate methods and analyze spatial data in their research.
Topics: This course covers statistical methods for georeferenced data (such as ozone measurements from different monitoring stations), spatial point patterns (such as incidents of plant/human disease), and areal data (such as county statistics in the US). An incomplete list of topics is as follows.
- Stationarity and variogram models
- Fitting a variogram model
- Kriging or best linear unbiased prediction (simple kriging, cokriging, and universal kriging).
- Kriging with large datasets
- Non-stationary models
- Spatio-temporal models
- Multivariate methods (direct and cross covariograms, cokriging)
- Conditional autoregressive models
- Spatial point patterns
- Complete randomness and Poisson processes
- Cluster processes and inhibition processes
- K-function
- Intensity function and inhomogenous Poisson processes
- Scan statistics
- Simulation of spatial processes and spatial point patterns
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STAT 638 Stochastic Processes I (MA 638) (Banner Course Number: 63800)
Semester: Fall
Prerequisites: STAT 539.
Credits: 3
Primary Audience:
Description: Advanced topics in probability theory which may include stationary processes, independent increment processes, Gaussian processes; martingales, Markov processes, ergodic theory.
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Semester: Fall
Prerequisites: STAT 539.
Credits: 3
Primary Audience:
Description: Advanced topics in probability theory which may include stationary processes, independent increment processes, Gaussian processes; martingales, Markov processes, ergodic theory.
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STAT 690 Seminar (Banner Course Number: 69000)
Semester: Fall Spring Summer
Prerequisites: Designator Required Course: Students must contact the department office toobtain a two digit instructor designator code.
Credits: 1-3
Primary Audience:
Description: Individual Study
Schedule and Textbook Information for Fall 2008
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Semester: Fall Spring Summer
Prerequisites: Designator Required Course: Students must contact the department office toobtain a two digit instructor designator code.
Credits: 1-3
Primary Audience:
Description: Individual Study
Schedule and Textbook Information for Fall 2008
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STAT 690M Mathematical Statistics Seminar (Banner Course Number: 69000)
Semester: Fall
Prerequisites:
Credits: 1
Primary Audience: Graduate students and faculty
Description: The goal of these seminars is to provide a platform for faculty members and graduate students to discuss important problems in contemporary mathematical statistics as well as their on-going research. At the end of the seminars on a particular theme, groups of people with common interest in research problems in that topic are expected to work together. The form of the seminars is intended to be informal, so that attendees can interact with each other freely. Graduate students may have opportunities to give presentations.
Schedule and Textbook Information for Fall 2008
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Semester: Fall
Prerequisites:
Credits: 1
Primary Audience: Graduate students and faculty
Description: The goal of these seminars is to provide a platform for faculty members and graduate students to discuss important problems in contemporary mathematical statistics as well as their on-going research. At the end of the seminars on a particular theme, groups of people with common interest in research problems in that topic are expected to work together. The form of the seminars is intended to be informal, so that attendees can interact with each other freely. Graduate students may have opportunities to give presentations.
Schedule and Textbook Information for Fall 2008
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STAT 690M.F07 Mathematical Statistics Seminar (Banner Course Number: 69000)
Semester: Fall
Prerequisites: None
Credits: 1
Primary Audience: Graduate students and faculty
Description: The goal of these seminars is to provide a platform for faculty members and graduate students to discuss important problems in contemporary mathematical statistics as well as their on-going research. At the end of the seminars on a particular theme, groups of people with common interest in research problems in that topic are expected to work together. The form of the seminars is intended to be informal, so that attendees can interact with each other freely.
Graduate students may have opportunities to give presentations. The focal topics in this semester temporarily are 1) detection of very weak signal in signal plus background model, 2) general nonparametrics, 3) Bayesian theory, and 4) multiple testing and false discovery rates.
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Semester: Fall
Prerequisites: None
Credits: 1
Primary Audience: Graduate students and faculty
Description: The goal of these seminars is to provide a platform for faculty members and graduate students to discuss important problems in contemporary mathematical statistics as well as their on-going research. At the end of the seminars on a particular theme, groups of people with common interest in research problems in that topic are expected to work together. The form of the seminars is intended to be informal, so that attendees can interact with each other freely.
Graduate students may have opportunities to give presentations. The focal topics in this semester temporarily are 1) detection of very weak signal in signal plus background model, 2) general nonparametrics, 3) Bayesian theory, and 4) multiple testing and false discovery rates.
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STAT 690M.SP07 Measure Theory Concepts Seminar (Banner Course Number: 69000)
Semester: Spring
Prerequisites:
Credits: 1
Primary Audience: Ph.D. statistics students
Description: This one-credit seminar is designed to present the main concepts of measure and measure-theoretic probability, including the important theorems. Details of arguments will be largely left out, for the attendee to fill in either by himself or from books. The student should have at least the equivalent of an undergraduate course in probability.
This seminar will not have the details expected from a course; the student will have to either get them from a course, or by independent study. Also, there are differences in different approaches, and only some of these can be discussed.
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Semester: Spring
Prerequisites:
Credits: 1
Primary Audience: Ph.D. statistics students
Description: This one-credit seminar is designed to present the main concepts of measure and measure-theoretic probability, including the important theorems. Details of arguments will be largely left out, for the attendee to fill in either by himself or from books. The student should have at least the equivalent of an undergraduate course in probability.
This seminar will not have the details expected from a course; the student will have to either get them from a course, or by independent study. Also, there are differences in different approaches, and only some of these can be discussed.
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STAT 691 Seminar in Probability Theory (Banner Course Number: 69100)
Semester: Fall Spring Summer
Prerequisites: Designator Required Course: Students must contact the departmental office to obtain a two digit instructor designator code.
Credits: 1-3
Primary Audience:
Description: Individual Study
Schedule and Textbook Information for Fall 2008
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Semester: Fall Spring Summer
Prerequisites: Designator Required Course: Students must contact the departmental office to obtain a two digit instructor designator code.
Credits: 1-3
Primary Audience:
Description: Individual Study
Schedule and Textbook Information for Fall 2008
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STAT 691M.SP07 Mathematical Statistics Seminar (Banner Course Number: 69100)
Semester: Spring
Prerequisites: None
Credits: 2
Primary Audience: Graduate students and faculty
Description: The goal of these seminars is to provide a platform for faculty members and graduate students to discuss important problems in contemporary mathematical statistics as well as their on-going research. At the end of the seminars on a particular theme, groups of people with common interest in research problems in that topic are expected to work together. The form of the seminars is intended to be informal, so that attendees can interact with each other freely.
Graduate students may have opportunities to give presentations. The focal topics in this semester temporarily are 1) detection of very weak signal in signal plus background model, 2) general nonparametrics, 3) Bayesian theory, and 4) multiple testing and false discovery rates.
The seminar series will be organized by Professor Tonglin Zhang.
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Semester: Spring
Prerequisites: None
Credits: 2
Primary Audience: Graduate students and faculty
Description: The goal of these seminars is to provide a platform for faculty members and graduate students to discuss important problems in contemporary mathematical statistics as well as their on-going research. At the end of the seminars on a particular theme, groups of people with common interest in research problems in that topic are expected to work together. The form of the seminars is intended to be informal, so that attendees can interact with each other freely.
Graduate students may have opportunities to give presentations. The focal topics in this semester temporarily are 1) detection of very weak signal in signal plus background model, 2) general nonparametrics, 3) Bayesian theory, and 4) multiple testing and false discovery rates.
The seminar series will be organized by Professor Tonglin Zhang.
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STAT 691M.SP08 Mathematical Statistics Seminar
(Banner Course Number: 69100)
Semester: Spring
Prerequisites:
Credits: 1
Primary Audience: Graduate students and faculty
Description: The goal of these seminars is to provide a platform for faculty members and graduate students to discuss important problems in contemporary mathematical statistics as well as their on-going research. At the end of the seminars on a particular theme, groups of people with common interest in research problems in that topic are expected to work together. The form of the seminars is intended to be informal, so that attendees can interact with each other freely. Graduate students may have opportunities to give presentations.
The seminar series will be organized by Professor Bowei Xi.
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Semester: Spring
Prerequisites:
Credits: 1
Primary Audience: Graduate students and faculty
Description: The goal of these seminars is to provide a platform for faculty members and graduate students to discuss important problems in contemporary mathematical statistics as well as their on-going research. At the end of the seminars on a particular theme, groups of people with common interest in research problems in that topic are expected to work together. The form of the seminars is intended to be informal, so that attendees can interact with each other freely. Graduate students may have opportunities to give presentations.
The seminar series will be organized by Professor Bowei Xi.
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STAT 691S Probability Seminar (Banner Course Number: 69100)
Semester: Fall Spring
Prerequisites:
Credits: 1
Primary Audience:
Description:
Schedule and Textbook Information for Fall 2008
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Semester: Fall Spring
Prerequisites:
Credits: 1
Primary Audience:
Description:
Schedule and Textbook Information for Fall 2008
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STAT 692 Statistics General Colloquium (Banner Course Number: 69200)
Semester: Fall Spring
Prerequisites:
Credits: 1
Primary Audience:
Description: Meets at same time and place as Research Colloquia. Previously listed as 690S.
Schedule and Textbook Information for Fall 2008
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Semester: Fall Spring
Prerequisites:
Credits: 1
Primary Audience:
Description: Meets at same time and place as Research Colloquia. Previously listed as 690S.
Schedule and Textbook Information for Fall 2008
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STAT 695 Seminar in Mathematical Statistics (Banner Course Number: 69500)
Semester: Fall Spring Summer
Prerequisites:
Credits: 1-3
Primary Audience:
Description: Individual Study that meets 3 times per week for 50 minutes per meeting for 16 weeks.
Schedule and Textbook Information for Fall 2008
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Semester: Fall Spring Summer
Prerequisites:
Credits: 1-3
Primary Audience:
Description: Individual Study that meets 3 times per week for 50 minutes per meeting for 16 weeks.
Schedule and Textbook Information for Fall 2008
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STAT 695B.F07 Computer Systems and Languages for Data Analysis (Banner Course Number: 69500)
Semester: Fall
Prerequisites: Basic statistics; math through advanced calculus and linear algebra; some knowledge of R helpful but not necessary.
Credits: 3
Primary Audience: Grad students in years 2 and beyond.
Description: Today's data analyst must know more than just an interactive software environment and must be ready to embrace a wide range of systems and languages. The course will treat a few of these. R and linux commands and shell programming will be included. Depending on the background and interests of participants, others will be selected from Perl, Python, MySQL, C, or any other language or system useful for programming with data.
Participants will break up into groups and each group will prepare lectures on some software topic. The course will begin with lectures from the instructor on graphics programming in R and then participants will present remaining topics.
Participants will be expected to write and run programs in linux related to their topics. A linux server will be made available for the course.
Detailed Description: Large, complex data sets are ubiquitous, the standard now rather than the exception. They present challenging problems of analysis because of their size and the complexity of their data structures and patterns. One approach is to compute summary statistics at the outset to reduce the complexity, but this expedient risks losing important information in the data. The goal should be lossless analysis: analyze the data at a level of detail and comprehensiveness that does not sacrifice information.
To achieve the goal of lossless analysis, for large data sets and small, we must be able to carry out sequential decision making. Data analysis cannot be mapped out start to stop from the beginning. It must be sequential, with decisions made at an earlier stage affecting later decisions.
The sequential analysis calls for an interactive software environment in which we get rapid responses to commands of statistical and machine learning that show us properties of the data. We want the environment to have a language for programming with data that allows (1) easy specification of commands for methods and managing data, (2) off-the-shelf methods of analysis, and (3) extensibility to allow analyses to be tailored to the data.
For large, complex data sets, commands of the interactive environment need to spawn computations in a core distributed computing environment with fast processors, large amounts of disk, and large memories. The core software needs to be able to run in parallel when possible across the environment. For specific learning methods not readily parallelized, it must run on subsets of the data that do not fit into memory. The methods need to be developed with a view toward the computational realities, and the methods need computational algorithms that reduce processor time as much as possible. Part of the computational algorithm needs to be a careful design of data structures accessed by the method.
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Semester: Fall
Prerequisites: Basic statistics; math through advanced calculus and linear algebra; some knowledge of R helpful but not necessary.
Credits: 3
Primary Audience: Grad students in years 2 and beyond.
Description: Today's data analyst must know more than just an interactive software environment and must be ready to embrace a wide range of systems and languages. The course will treat a few of these. R and linux commands and shell programming will be included. Depending on the background and interests of participants, others will be selected from Perl, Python, MySQL, C, or any other language or system useful for programming with data.
Participants will break up into groups and each group will prepare lectures on some software topic. The course will begin with lectures from the instructor on graphics programming in R and then participants will present remaining topics.
Participants will be expected to write and run programs in linux related to their topics. A linux server will be made available for the course.
Detailed Description: Large, complex data sets are ubiquitous, the standard now rather than the exception. They present challenging problems of analysis because of their size and the complexity of their data structures and patterns. One approach is to compute summary statistics at the outset to reduce the complexity, but this expedient risks losing important information in the data. The goal should be lossless analysis: analyze the data at a level of detail and comprehensiveness that does not sacrifice information.
To achieve the goal of lossless analysis, for large data sets and small, we must be able to carry out sequential decision making. Data analysis cannot be mapped out start to stop from the beginning. It must be sequential, with decisions made at an earlier stage affecting later decisions.
The sequential analysis calls for an interactive software environment in which we get rapid responses to commands of statistical and machine learning that show us properties of the data. We want the environment to have a language for programming with data that allows (1) easy specification of commands for methods and managing data, (2) off-the-shelf methods of analysis, and (3) extensibility to allow analyses to be tailored to the data.
For large, complex data sets, commands of the interactive environment need to spawn computations in a core distributed computing environment with fast processors, large amounts of disk, and large memories. The core software needs to be able to run in parallel when possible across the environment. For specific learning methods not readily parallelized, it must run on subsets of the data that do not fit into memory. The methods need to be developed with a view toward the computational realities, and the methods need computational algorithms that reduce processor time as much as possible. Part of the computational algorithm needs to be a careful design of data structures accessed by the method.
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STAT 695C.SP07 Statistics and Visualization for Computer Networking and Healthcare Engineering (Banner Course Number: 69500)
Semester: Spring
Prerequisites: Permission of Instructor is required.
Credits: 3
Primary Audience: Advanced statistics graduate students carrying out research in computer networking or healthcare engineering.
Description: This advanced research course will focus on the use of statistical and visualization methods for two different fields of engineering: computer networking and healthcare engineering. Students responsibilities will be analyze data in one of these two areas and develop new tools for their analysis.
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Semester: Spring
Prerequisites: Permission of Instructor is required.
Credits: 3
Primary Audience: Advanced statistics graduate students carrying out research in computer networking or healthcare engineering.
Description: This advanced research course will focus on the use of statistical and visualization methods for two different fields of engineering: computer networking and healthcare engineering. Students responsibilities will be analyze data in one of these two areas and develop new tools for their analysis.
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STAT 695D Introduction to Data Visualization (Banner Course Number: 69500)
Semester: Fall
Prerequisites: Knowledge of basic probability, mathematics through calculus and linear algebra, and basic statistics including least-squares fitting of parametric functions to data. No previous knowledge of data visualization is needed.
Credits: 3
Primary Audience: Graduate students in university departments where data are analyzed.
Description: All areas of learning from data -- statistics, machine learning, and data mining -- can benefit immensely from data visualization.
Graphs allow us to explore data to see overall patterns and to see detailed behavior; no other approach can compete in revealing the structure of data so thoroughly. Graphs allow us to view complex mathematical models fitted to data, and they allow us to assess the validity of such models. But realizing the potential of data visualization requires methods and basic principles.
The course is about methods and basic principles that help the data analyst to realize the potential of visualization. The material is divided into principles of graph construction, graphical methods, and graphical perception.
Each student will receive two books of the instructor, The Elements of Graphing Data and Visualizing Data, from which some of the material in the course will be taken. Other material will come from research papers or from new ideas and concepts documented in viewgraphs.
Schedule and Textbook Information for Fall 2008
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Course Page for Fall 2008
Semester: Fall
Prerequisites: Knowledge of basic probability, mathematics through calculus and linear algebra, and basic statistics including least-squares fitting of parametric functions to data. No previous knowledge of data visualization is needed.
Credits: 3
Primary Audience: Graduate students in university departments where data are analyzed.
Description: All areas of learning from data -- statistics, machine learning, and data mining -- can benefit immensely from data visualization.
Graphs allow us to explore data to see overall patterns and to see detailed behavior; no other approach can compete in revealing the structure of data so thoroughly. Graphs allow us to view complex mathematical models fitted to data, and they allow us to assess the validity of such models. But realizing the potential of data visualization requires methods and basic principles.
The course is about methods and basic principles that help the data analyst to realize the potential of visualization. The material is divided into principles of graph construction, graphical methods, and graphical perception.
Each student will receive two books of the instructor, The Elements of Graphing Data and Visualizing Data, from which some of the material in the course will be taken. Other material will come from research papers or from new ideas and concepts documented in viewgraphs.
Schedule and Textbook Information for Fall 2008
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Course Page for Fall 2008
STAT 695F Malliavin Calculus (Banner Course Number: 69500)
Semester: Fall
Prerequisites: Instructor approval is required.
Credits: 3
Primary Audience: Advanced Ph.D. students in Statistics and Mathematics working on probability & stochastic analysis. Also for Ph.D. students in Econ, Finance, & Engineering, with measure thry and probab. training
Description: This advanced graduate course on the Malliavin calculus requires a good knowledge of stochastic calculus with respect to Brownian motion (such as MA/STAT 638), and some familiarity with Gaussian processes. It covers basic definitions of the Malliavin derivative and the Skorohod (divergence) integral, Wiener chaos, applications to smoothness of probability laws, to integration with respect to fractional Brownian motion, and to arbitrage theory in continuous-time stochastic finance.
Schedule and Textbook Information for Fall 2008
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Semester: Fall
Prerequisites: Instructor approval is required.
Credits: 3
Primary Audience: Advanced Ph.D. students in Statistics and Mathematics working on probability & stochastic analysis. Also for Ph.D. students in Econ, Finance, & Engineering, with measure thry and probab. training
Description: This advanced graduate course on the Malliavin calculus requires a good knowledge of stochastic calculus with respect to Brownian motion (such as MA/STAT 638), and some familiarity with Gaussian processes. It covers basic definitions of the Malliavin derivative and the Skorohod (divergence) integral, Wiener chaos, applications to smoothness of probability laws, to integration with respect to fractional Brownian motion, and to arbitrage theory in continuous-time stochastic finance.
Schedule and Textbook Information for Fall 2008
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STAT 695F.SP07 Malliavin Calculus (Banner Course Number: 69500)
Semester: Spring
Prerequisites: Instructor approval is required.
Credits: 3
Primary Audience: Advanced Ph.D. students in Statistics and Mathematics working on probability & stochastic analysis. Also for Ph.D. students in Econ, Finance, & Engineering, with measure thry and probab. training.
Description: This advanced graduate course on the Malliavin calculus requires a good knowledge of stochastic calculus with respect to Brownian motion (such as MA/STAT 638), and some familiarity with Gaussian processes. It covers basic definitions of the Malliavin derivative and the Skorohod (divergence) integral, Wiener chaos, applications to smoothness of probability laws, to integration with respect to fractional Brownian motion, and to arbitrage theory in continuous-time stochastic finance.
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Semester: Spring
Prerequisites: Instructor approval is required.
Credits: 3
Primary Audience: Advanced Ph.D. students in Statistics and Mathematics working on probability & stochastic analysis. Also for Ph.D. students in Econ, Finance, & Engineering, with measure thry and probab. training.
Description: This advanced graduate course on the Malliavin calculus requires a good knowledge of stochastic calculus with respect to Brownian motion (such as MA/STAT 638), and some familiarity with Gaussian processes. It covers basic definitions of the Malliavin derivative and the Skorohod (divergence) integral, Wiener chaos, applications to smoothness of probability laws, to integration with respect to fractional Brownian motion, and to arbitrage theory in continuous-time stochastic finance.
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STAT 695G Objective Bayesian Analysis (Banner Course Number: 69500)
Semester: Fall
Prerequisites: STAT 519 and STAT 528
Credits: 3
Primary Audience: Doctoral students
Description: Evaluation is through three sets of assignments that would give you a choice from a broad spectrum of problems, on numerical computation simulation, mathematical or theoretical problems and applied or methodological questions.
There is no textbook for the course. However, I will use An Introduction to Bayesian Analysis Theory and Methods by Ghosh, Delampady and Samanta (Springer 2006) as a reference. You don't need to buy the book. I will give notes and have a copy of notes (in the course file in the Math Library) as we go along.
Objective Bayesian Analysis Syllabus
Schedule and Textbook Information for Fall 2008
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Semester: Fall
Prerequisites: STAT 519 and STAT 528
Credits: 3
Primary Audience: Doctoral students
Description: Evaluation is through three sets of assignments that would give you a choice from a broad spectrum of problems, on numerical computation simulation, mathematical or theoretical problems and applied or methodological questions.
There is no textbook for the course. However, I will use An Introduction to Bayesian Analysis Theory and Methods by Ghosh, Delampady and Samanta (Springer 2006) as a reference. You don't need to buy the book. I will give notes and have a copy of notes (in the course file in the Math Library) as we go along.
Objective Bayesian Analysis Syllabus
- Decision theoretic formulation of statistical Inference. Predictive formulation of statistical Inference to the three paradigms of statistics: Classical statistics, Bayesian Analysis, Data Mining/Machine Learning/Statistical Learning, Paradoxes in classical statistics, Likelihood Principle (LP), coherence. Birnmaum's theorem on LP, Finetti's theorem on coherence. Rationality principles leading to Bayesian Analysis Subjective and Objective Bayesian Analysis.
- Choice of priors, de Finetti's representation theorem on exchangeable sequence, algorithmic constructions of objective priors, common criticism of such priors and answers, elicitation of a subjective prior. Introduction to common inference problems, BIC, MCMC
- Laplace approximation, Bayesian asymptotics, Frequentist validation of Bayesian Analysis via posterior consistency, Schwartz's theorem on posterior consistence (without proof), Posterior normality, the Kadane-Kass-Tierney approximations to posterior calculations. Derivation of BIC via Laplace approximation. Choice of sample size for Bayesian testing problems.
- Bayesian Testing for sharp null and composite null. Comparison and P-values and posterior probabilities, the BBS (Bayarri-Berger-Sellke) calibration of P-values for sharp nulls, Bayesian P-values,
- Difficulties in Objective Bayes testing, the Berger Pericchi solution through Intrinsic Bayes Analysis, Intrinsic Priors, O'Hagan's Fractional Bayes Analysis, A comprehensive approach to these methods.
- High dimensional, Parametric and Hierarchical, Bayesian problems (Estimation, Testing, Model selection). High dimensional Bayesian testing and MCMC
- Three applications (Disease mapping via spatial Bayesian analysis, Bayesian nonparametric regression via wavelets and Dirichlet multinomial allocation)
Schedule and Textbook Information for Fall 2008
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STAT 695G.F07 Survival Analysis (Banner Course Number: 69500)
Semester: Fall
Prerequisites: STAT 519 and STAT 528.
Credits: 3
Primary Audience:
Description: Offered Fall 2007
In the first part I will focus on likelihood based and Bayesian methods of nonparametric survival analysis, mostly for right censored data. Topics will include Kaplan-Meier, the Cox regression model, model diagnostics, goodness of fit tests, mostly taken up from the book by Klein and Moeshberger (Springer), supplemented with my notes.
In the second part I will discuss double and interval censoring.This part would be based on Groneboom's work. Grades would be based on HW's, which would include theoretical, computational and simulation based problems.
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Semester: Fall
Prerequisites: STAT 519 and STAT 528.
Credits: 3
Primary Audience:
Description: Offered Fall 2007
In the first part I will focus on likelihood based and Bayesian methods of nonparametric survival analysis, mostly for right censored data. Topics will include Kaplan-Meier, the Cox regression model, model diagnostics, goodness of fit tests, mostly taken up from the book by Klein and Moeshberger (Springer), supplemented with my notes.
In the second part I will discuss double and interval censoring.This part would be based on Groneboom's work. Grades would be based on HW's, which would include theoretical, computational and simulation based problems.
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STAT 695K.F07 Malliavin Calculus II (Banner Course Number: 69500)
Semester: Fall
Prerequisites: Malliavin Calculus (STAT 695F.SP07)
Credits: 3
Primary Audience: Advanced Ph.D. students in Statistics and Mathematics working on probability & stochastic analysys. Also for Ph.D. students in Econ, Finance, & Engineering, with measure thry. and probab. training
Description: Continuation of Malliavin Calculus (STAT 695F.SP07). This semester, we will cover more advanced topics in the applications of the Malliavin calculus and other aspects of Stochastic Analysis, including stochastic ordinary and partial differential equations and smoothness of their probability laws, integration and stochastic differential equations driven by fractional Brownian motion, hedging and insider trading for financial mathematics, and long-memory parameter estimation for Wiener chaos processes.
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Semester: Fall
Prerequisites: Malliavin Calculus (STAT 695F.SP07)
Credits: 3
Primary Audience: Advanced Ph.D. students in Statistics and Mathematics working on probability & stochastic analysys. Also for Ph.D. students in Econ, Finance, & Engineering, with measure thry. and probab. training
Description: Continuation of Malliavin Calculus (STAT 695F.SP07). This semester, we will cover more advanced topics in the applications of the Malliavin calculus and other aspects of Stochastic Analysis, including stochastic ordinary and partial differential equations and smoothness of their probability laws, integration and stochastic differential equations driven by fractional Brownian motion, hedging and insider trading for financial mathematics, and long-memory parameter estimation for Wiener chaos processes.
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STAT 695N.SP07 Topics in Machine Learning (Banner Course Number: 69500)
Semester: Spring
Prerequisites: STAT 516, STAT 517 or equivalent. Familiarity with programming.
Credits: 3
Primary Audience: Graduate Students in Statistics, Engineering, and Computer Science
Description: The course will explore recently found connections between information theory, statistics, and machine learning. The first part of the course will cover basic information theory and the second part will cover advanced topics and connections to statistics and machine learning. For more information, please see the course page and the syllabus found at Professor Lebanon's web site.
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Semester: Spring
Prerequisites: STAT 516, STAT 517 or equivalent. Familiarity with programming.
Credits: 3
Primary Audience: Graduate Students in Statistics, Engineering, and Computer Science
Description: The course will explore recently found connections between information theory, statistics, and machine learning. The first part of the course will cover basic information theory and the second part will cover advanced topics and connections to statistics and machine learning. For more information, please see the course page and the syllabus found at Professor Lebanon's web site.
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STAT 695S Statistical Inference and Belief Functions (Banner Course Number: 69500)
Semester: Fall
Prerequisites: STAT 511 and STAT 512
Credits: 3
Primary Audience: Graduate Students
Description: Two fundamental and contrasting approaches to statistical inference, namely, inference based on sampling distributions and inference based on posterior distributions are reviewed and illustrated with practical examples. Parameter estimation, (multiple) hypothesis testing, and prediction are discussed. The course focuses on current research topics on "belief functions", which is also known as the Dempster-Shafer (DS) theory. The topics include the Weak Belief principle proposed recently by C. Liu and J. Zhang for building credible DS-type models for very-high-dimensional data in particular. Applications of methods from different schools of thought to be scrutinized include statistical deconvolution, multiple testing, variable selection, variance component models, multivariate normal models, multinomial models, and non-parametric problems.
Schedule and Textbook Information for Fall 2008
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Semester: Fall
Prerequisites: STAT 511 and STAT 512
Credits: 3
Primary Audience: Graduate Students
Description: Two fundamental and contrasting approaches to statistical inference, namely, inference based on sampling distributions and inference based on posterior distributions are reviewed and illustrated with practical examples. Parameter estimation, (multiple) hypothesis testing, and prediction are discussed. The course focuses on current research topics on "belief functions", which is also known as the Dempster-Shafer (DS) theory. The topics include the Weak Belief principle proposed recently by C. Liu and J. Zhang for building credible DS-type models for very-high-dimensional data in particular. Applications of methods from different schools of thought to be scrutinized include statistical deconvolution, multiple testing, variable selection, variance component models, multivariate normal models, multinomial models, and non-parametric problems.
Schedule and Textbook Information for Fall 2008
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STAT 695S.F07 Modern Geospatial Statistics (Banner Course Number: 69500)
Semester: Fall
Prerequisites: STAT 517, and basic calculus and linear algebra
Credits: 3
Primary Audience: Graduate studens in year 2 and beyond
Description: Geospatial data refer to observations referenced by the particular locations where the observations are made. An example of geospatial data is air temperatures measured at different weather stations, where the locations are referenced by longitude and latitude. Therefore geospatial data are also referred to as georeferenced data. Such georeferenced data arise frequently in agricultural sciences, environmental studies, forestry and natural resources, ecology, geophysics, hydrology, and many other disciplines, and are usually spatially correlated in the sense that data from locations that are close to each other are likely to be similar. Traditional geostatistics is primarily focused on best linear unbiased prediction (BLUP) by employing the spatial correlation. Modern geostatistics goes much more beyond BLUP to include estimation theory, asymptotics, computing, spatial sampling designs, model-based geostatistics and multivariate extensions.
This course provides a review of traditional geostatistics and covers recent advances in modern spatial statistics of single and multivariate variables. One objective is for students to become familiar with the literature and to prepare students to carry out research in spatial statistics to meet the increasing needs of analyzing massive spatial data.
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Semester: Fall
Prerequisites: STAT 517, and basic calculus and linear algebra
Credits: 3
Primary Audience: Graduate studens in year 2 and beyond
Description: Geospatial data refer to observations referenced by the particular locations where the observations are made. An example of geospatial data is air temperatures measured at different weather stations, where the locations are referenced by longitude and latitude. Therefore geospatial data are also referred to as georeferenced data. Such georeferenced data arise frequently in agricultural sciences, environmental studies, forestry and natural resources, ecology, geophysics, hydrology, and many other disciplines, and are usually spatially correlated in the sense that data from locations that are close to each other are likely to be similar. Traditional geostatistics is primarily focused on best linear unbiased prediction (BLUP) by employing the spatial correlation. Modern geostatistics goes much more beyond BLUP to include estimation theory, asymptotics, computing, spatial sampling designs, model-based geostatistics and multivariate extensions.
This course provides a review of traditional geostatistics and covers recent advances in modern spatial statistics of single and multivariate variables. One objective is for students to become familiar with the literature and to prepare students to carry out research in spatial statistics to meet the increasing needs of analyzing massive spatial data.
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STAT 695V.SP08 Data Visualization (Banner Course Number: 69500)
Semester: Spring
Prerequisites: Knowledge of basic probability, mathematics through calculus and linear algebra, and basic statistics including least-squares fitting of parametric functions to data. No previous knowledge of data visualization is needed.
Credits: 3
Primary Audience: Graduate students in university departments where data are analyzed.
Description: All areas of learning from data -- statistics, machine learning, and data mining -- can benefit immensely from data visualization. Visualization provides a front line of attack in the analysis of data, revealing intricate structure that cannot be absorbed in any other way. We discover unimagined effects, and we challenge hypothesized ones.
Many approaches to learning from data today involve the use of complex methods and models and that to some extent tailor themselves to the patterns of the data, a form of automated learning. But human guidance from data visualizations added to this can vastly increase understanding of the data and the performance of the models and methods.
The course will present aproaches, tools, and software for data visualization. Each student will study a topic in data visualization and give a presentation to the class on the area. Initial lectures will be given by the instructor, and student presentations will occur toward the end of the semester.
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Semester: Spring
Prerequisites: Knowledge of basic probability, mathematics through calculus and linear algebra, and basic statistics including least-squares fitting of parametric functions to data. No previous knowledge of data visualization is needed.
Credits: 3
Primary Audience: Graduate students in university departments where data are analyzed.
Description: All areas of learning from data -- statistics, machine learning, and data mining -- can benefit immensely from data visualization. Visualization provides a front line of attack in the analysis of data, revealing intricate structure that cannot be absorbed in any other way. We discover unimagined effects, and we challenge hypothesized ones.
Many approaches to learning from data today involve the use of complex methods and models and that to some extent tailor themselves to the patterns of the data, a form of automated learning. But human guidance from data visualizations added to this can vastly increase understanding of the data and the performance of the models and methods.
The course will present aproaches, tools, and software for data visualization. Each student will study a topic in data visualization and give a presentation to the class on the area. Initial lectures will be given by the instructor, and student presentations will occur toward the end of the semester.
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STAT 695Z.SP07 Dimension Reduction and Variable Selection in Regression (Banner Course Number: 69500)
Semester: Spring
Prerequisites: STAT 512 or STAT 525, STAT 517 or STAT 528.
Credits: 3
Primary Audience: Graduate Students
Description: Dimension reduction has received much attention lately across many areas due to the popularity of high dimensional data as well as the curse of dimensionality. This course is an introduction to existing methods and theory of dimension reduction in the regression setting. Most course material will be adopted directly from the literature on dimension reduction, and students are expected to actively participate in the course by reviewing and presenting papers.
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Semester: Spring
Prerequisites: STAT 512 or STAT 525, STAT 517 or STAT 528.
Credits: 3
Primary Audience: Graduate Students
Description: Dimension reduction has received much attention lately across many areas due to the popularity of high dimensional data as well as the curse of dimensionality. This course is an introduction to existing methods and theory of dimension reduction in the regression setting. Most course material will be adopted directly from the literature on dimension reduction, and students are expected to actively participate in the course by reviewing and presenting papers.
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STAT 698 Research MS Thesis (Banner Course Number: 69800)
Semester: Fall Spring Summer
Prerequisites:
Credits: 1-18
Primary Audience:
Description:
Schedule and Textbook Information for Fall 2008
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Semester: Fall Spring Summer
Prerequisites:
Credits: 1-18
Primary Audience:
Description:
Schedule and Textbook Information for Fall 2008
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STAT 699 Research PhD Thesis (Banner Course Number: 69900)
Semester: Fall Spring Summer
Prerequisites:
Credits: 1-18
Primary Audience:
Description:
Schedule and Textbook Information for Fall 2008
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Semester: Fall Spring Summer
Prerequisites:
Credits: 1-18
Primary Audience:
Description:
Schedule and Textbook Information for Fall 2008
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STAT 699A Research PhD Thesis Absentia (Banner Course Number: 69900)
Semester: Fall Spring Summer
Prerequisites:
Credits: 3-18
Primary Audience:
Description:
Schedule and Textbook Information for Fall 2008
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Semester: Fall Spring Summer
Prerequisites:
Credits: 3-18
Primary Audience:
Description:
Schedule and Textbook Information for Fall 2008
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STAT 699B Research PhD Thesis Absentia (Banner Course Number: 69900)
Semester: Fall Spring Summer
Prerequisites:
Credits: 3-18
Primary Audience:
Description:
Schedule and Textbook Information for Fall 2008
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Semester: Fall Spring Summer
Prerequisites:
Credits: 3-18
Primary Audience:
Description:
Schedule and Textbook Information for Fall 2008
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