Statistics 526
Advanced Statistical Methodology
Spring 2017
Instructor: Chong Gu
Classes: 1:30  2:20 MWF, REC 114
Office Hours: 2:30  3:30 MWF, HAAS 170, or by appointment
The final will be on Monday, May 1, 13pm, in REC 114. You can bring 4 lettersize, doublesided crib sheets, and a calculator; no mobile devices. Here is an old final.
 Course outline
 As a sequel to STAT 525, this course introduces some statistical
modeling tools that are developed for situations where least
squares regression and standard ANOVA techniques may not
naturally apply. Our coverage centers around two lines of models
that are closely related, the generalized linear models (GLM) for
regression (and ANOVA) with non Gaussian responses, and survival
models for the analysis of lifetime data. Among issues to be
discussed are the estimation of the models, the testing of
hypotheses, and the checking of model adequacy. Data examples
will be used throughout the course to illustrate the
methodologies and the related software tools.
 Prerequisite
 Working knowledge of basic statistical inference and modeling,
such as the maximum likelihood estimate, the likelihood ratio
test, and the standard linear models.
 Textbook

 An introduction to generalized linear models (3rd
ed.), by Dobson and Barnett.
 References

 Modern
Applied Statistics with SPlus (4th ed), by Venables & Ripley.
 Extending the Linear Model with R, by Faraway.
 Applying Generalized Linear Models, by Lindsey.
 Generalized Linear Models (2nd ed), by McCullagh & Nelder.
 The Statistical Analysis of Failure Time Data (2nd ed),
by Kalbfleisch and Prentice.
 Applied Survival Analysis, by Le.
 Software
 We will be using R, an opensource environment
not unlike S/Splus, as the primary platform for computation and
graphics. R resources are to be found at
CRAN, the Comprehensive R
Archive Network.
The following tutorial document should be helpful to you,
especially if you had little previous exposure to R/S/Splus.
 Course Work
 There will be biweekly homework
assignments, a midterm, and a final. The assignments will
contribute about 40% to the course grade, the midterm 30%, and
the final 30%. You are encouraged to discuss with each other on
the homework assignments, but you are expected to do your
independent work.
 Lecture Notes
 Homework Assignments
 