Graduate Course Descriptions

Regular courses

STAT 501 Experimental Statistics I (Banner Course Number: 50100)
Semester: Fall Spring Summer
Prerequisites: MA 153, MA 158, 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.

*For statistics majors and minors, credit should be allowed in no more than one of STAT 301, STAT 350, STAT 501, and in no more than one of STAT 503 and STAT 511.

Schedule and Textbook Information for Fall 2017
Course Page for Summer 2017

 

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 other introductory statistics course with some SAS experience). Does not require knowledge of calculus. Good for graduate students in a variety of disciplines whose research will require statistical analysis.

Schedule and Textbook Information for Fall 2017

 

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. JMP statistical software is used. Mathematical experience at the level of one semester of calculus is required, though no calculus is used in the course.

For statistics majors and minors, credit should be allowed in no more than one of STAT 301, STAT 350, STAT 501, and in no more than one of STAT 503 and STAT 511.

Schedule and Textbook Information for Fall 2017
 

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 CS 154 or CS 180 and a calculus-based introductory statistics course such as STAT 350, STAT 503, and STAT 511
Credits: 3
Primary Audience: Undergraduates in statistics or actuarial science; graduate students in applied statistics or other disciplines.
Description: 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.

Schedule and Textbook Information for Fall 2017
 

STAT 511 Statistical Methods (Banner Course Number: 51100)
Semester: Fall Spring Summer
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.

For statistics majors and minors, credit should be allowed in no more than one of STAT 301, STAT 350, STAT 501, and in no more than one of STAT 503 and STAT 511.

Schedule and Textbook Information for Fall 2017
Course Page for Spring 2017 Section 007

 

STAT 512 Applied Regression Analysis (Banner Course Number: 51200)
Semester: Fall Spring Summer
Prerequisites: STAT 503, STAT 511, or STAT 517 or STAT 350
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.

Schedule and Textbook Information for Fall 2017
Course Page for Spring 2017 Sections 001, 002 and 003

 

STAT 512Q Applied Regression Analysis (Banner Course Number: 51200)
Semester: Fall
Prerequisites: STAT 503, STAT 511, or STAT 517 or STAT 350
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.

Schedule and Textbook Information for Fall 2017

 

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: Graduate students and advanced undergraduates with an interest in the application of modern Quality Statistical Methodology coupled with modern Quality Management Techniques.
Description: Techniques of modern Quality Control and Management. Topics include Statistical and graphical data summaries, basic tools (pareto charts, fishbone diagrams, flowcharts), Control Charts for Measurement and Attribute data, proper use of Control Charts, Capability Studies, Continuous Improvement, ISO 9000:2008 Requirements, Six Sigma and Taguchi Methodology.
Course Objectives:To become fluent in the language and techniques of modern Quality Control and its applications. While areas of application are typically thought of as being in the area of manufacturing, they can also be applied to ordering, accounting, record keeping and customer satisfaction, among others.

Schedule and Textbook Information for Fall 2017

 

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.

Schedule and Textbook Information for Fall 2017

 

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.

Schedule and Textbook Information for Fall 2017

 

STAT 515 Statistical Consulting Problem (Banner Course Number: 51500)
Semester: Fall Spring Summer
Prerequisites: Admission by consent of instructor.
Credits: 1
Primary Audience: Students seeking an MS in Applied Statistics who have been involved with the Statistical Consulting Service
Description: Can be used with 2 semesters of STAT 597 to replace STAT 582. Typically taught in conjunction with STAT 582 but only meets one day a week. Topics include written and oral communication, research ethics, and application of statistical methods to real problems.

Schedule and Textbook Information for Fall 2017

 

STAT 516 Basic Probability and Applications (Banner Course Number: 51600)
Semester: Fall Spring
Prerequisites: MA 261, MA 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.

Schedule and Textbook Information for Fall 2017

 

STAT 516Q Basic Probability and Applications (Banner Course Number: 51600)
Semester: Fall Spring
Prerequisites: MA 261, MA 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.

Schedule and Textbook Information for Fall 2017

 

STAT 517 Statistical Inference (Banner Course Number: 51700)
Semester: Fall Spring
Prerequisites: STAT 516 or STAT 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.

Schedule and Textbook Information for Fall 2017
 

STAT 519 Introduction to Probability (MA 519) (Banner Course Number: 51900)
Semester: Fall Spring
Prerequisites: MA 510; or corequisite: MA 440 or MA 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.

Schedule and Textbook Information for Fall 2017

 

STAT 520 Time Series and Applications (Banner Course Number: 52000)
Semester: Spring
Prerequisites: STAT 516 and STAT 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.

Schedule and Textbook Information for Fall 2017

 

STAT 522 Sampling and Survey Techniques (Banner Course Number: 52200)
Semester: Spring
Prerequisites: STAT 512 or STAT 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.

Schedule and Textbook Information for Fall 2017

 

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.

Schedule and Textbook Information for Fall 2017
 

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 covered include multiple regression, analysis of variance for fixed, random and mixed designs. Logistic response models and hierarchical log linear models for contingency tables.

Schedule and Textbook Information for Fall 2017

 

STAT 526 Advanced Statistical Methodology (Banner Course Number: 52600)
Semester: Fall Spring
Prerequisites: STAT 525. Co-requisite STAT 528
Credits: 3
Primary Audience:
Description: The course introduces statistical models for situations where least squares regression and standard ANOVA techniques do not apply. Its objectives are (1) to conceptually understand log-linear and generalized linear models for count data, survival models for the analysis of lifetime data, and linear mixed effects models, (2) to use these models in application to real data, (3) draw valid conclusions and clearly present the results.

Schedule and Textbook Information for Fall 2017

 

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.

Schedule and Textbook Information for Fall 2017

 

STAT 529 Bayesian Applied Decision Theory (Banner Course Number: 52900)
Semester: Spring
Prerequisites: STAT 517 or equivalent
Credits: 3
Primary Audience: Applied Statisticians and other disciplines who use data to make decisions.
Description: 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.

Schedule and Textbook Information for Fall 2017

 

STAT 532 Elements of Stochastic Processes (MA 532) (Banner Course Number: 53200)
Semester: Fall 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.

Schedule and Textbook Information for Fall 2017

 

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.

Schedule and Textbook Information for Fall 2017

 

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.

Schedule and Textbook Information for Fall 2017 >

 

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 MA 364 or MA 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.

Schedule and Textbook Information for Fall 2017

 

STAT 541 Advanced Probability and Options, with Numerical Methods (MA 516; before 2005 listed as STAT 545) (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 graduate STAT 545. 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.

Schedule and Textbook Information for Fall 2017
 

STAT 545 Introduction to Computational Statistics (Banner Course Number: 54500)
Semester: Fall
Prerequisites: STAT 516, STAT 517 and an introductory programming course, e.g., CS 159/CS 177/CS 190C. 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 2017

 

STAT 546 Computational Statistics (Banner Course Number: 54600)
Semester: Spring
Prerequisites: STAT 545
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 frequentist 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.

Schedule and Textbook Information for Fall 2017

 

STAT 549 An Applied Intro. to QTL Mapping in Experimental Populations (Banner Course Number: 54900)
Semester: Fall
Prerequisites: STAT 511 and STAT 512
Credits: 3
Primary Audience: Interdisciplinary
Description: The detection of genomic regions that control quantitative traits 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. 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). 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 will be provided for the purpose of setting the stage for mapping natural variation in the context of functional genomics (i.e., expression QTL or e-QTL).

This is a graduate level interdisciplinary course that requires minimal computer experience, lower levels of statistical training are required (STAT 503 or STAT 511, and/or STAT 512), and a maximal level of motivation and participation. All students who are interested in genomics and/or statistical 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.

Schedule and Textbook Information for Fall 2017

 

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.

Schedule and Textbook Information for Fall 2017

 

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.

Schedule and Textbook Information for Fall 2017

 

STAT 580 Application of Statistical Theory (Banner Course Number: 58000)
Semester: Fall
Prerequisites: STAT 528 or equivalent and appropriate mathematical knowledge.
Credits: 3
Primary Audience: PhD students in statistics.
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 2017

 

STAT 582 Statistical Consulting and Collaboration (Banner Course Number: 58200)
Semester: Spring
Prerequisites: STAT 514 and STAT 525
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.

Schedule and Textbook Information for Fall 2017

 

STAT 598Z Introduction to Computing for Statisticians (Banner Course Number: 59800)
Semester: Spring
Prerequisites: Only masters students in the department of Statistics are allowed to enroll. All others require permission from the instructor.
Credits: 3
Primary Audience: MS students in statistics with little or no exposure to computing concepts and programming
Description: The objective of this course is to introduce concepts in computing to statisticians. The first part of the course will concentrate on introducing Python and teaching the students basic programming constructs. The next part will concentrate on working with these programming constructs for implementing statistical algorithms with emphasis on sampling and density estimation. The final part of the course will introduce students to convexity and convex optimization. Modern machine learning methods which use these concepts for analysis of large datasets will be briefly introduced.

The course will feature equal number of lectures and hands-on laboratory sessions. The exams will be a mix of theory and hands-on programming. Students will also get valuable experience in practical data analysis via a course project.

Schedule and Textbook Information for Fall 2017

 

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.

Schedule and Textbook Information for Fall 2017

 

STAT 639 Stochastic Processes II (MA 639) (Banner Course Number: 63900)
Semester: Spring
Prerequisites: MA/STAT 638 or a solid course in measure-theoretic stochastic calculus.
Credits: 3
Primary Audience:
Description: This is the continuation of MA/STAT 638. We will concentrate on specific chapters from the textbook, including Ch VI-IX (Local Times, Generators, Girsanov's theorem, Stochastic Differential Equations). Some material from another textbook (Karatzas and Shreve, Brownian Motion and Stochastic Calculus), and the instructor's own work, may also be used, especially to cover Feynman-Kac formulas and the connection to PDEs and Stochastic PDEs. New topics not treatable using martingales will also be investigated, include stochastic integration with respect to Fractional Brownian Motion and other, more irregular Gaussian processes; anticipative stochastic calculus; Gaussian and non-Gaussian regularity theory.

Schedule and Textbook Information for Fall 2017

 

Non-lecture courses

Internships

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.

Schedule and Textbook Information for Fall 2017

 

Seminars

STAT 597 Statistical Consulting Seminar (Banner Course Number: 59700)
Semester: Fall Spring Summer
Prerequisites: STAT 514, STAT 525, and consent of instructor
Credits: 1
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.

Schedule and Textbook Information for Fall 2017

 

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).

Schedule and Textbook Information for Fall 2017

 

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 2017

 

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 2017

 

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 2017

 

STAT 691S Probability Seminar (Banner Course Number: 69100)
Semester: Fall Spring
Prerequisites: It is recommended that the student has taken a graduate course in probability MA/STAT 538 and MA/STAT 539.
Credits: 1
Primary Audience:
Description: The Probability Seminar is a weekly seminar on recent advances in the field of probability theory. Speakers from within Purdue as well as visitors from other institutions will present their recent work. Topics covered often include Markov processes, random walks, Malliavin calculus, stochastic (partial) differential equations, martingales, as well as other topics in probability theory.

Schedule and Textbook Information for Fall 2017

 

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 2017

 

Dissertation research courses

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 2017

 

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 2017

 

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 2017

 

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 2017

Last Updated: Aug 25, 2017 2:42 PM

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