by James K. Lindsey,
New York: Springer-Verlag New York, Inc., 1997
by P. McCullagh and J. A. Nelder,
London: Chapman and Hall, 1989
Course Info site http://cinfo.aas.duke.edu/courses/STA216.01-F2000
Instructor: Merlise Clyde
| Office: | |
| Phone: | 681-8440 |
| Email: | clyde@stat.duke.edu |
| Office Hours: | Tues 2:30-3:30, Wed 3:35-5:00 or by appointment |
Meeting Times: Monday & Wednesday, 2:20 - 3:35, Room 232 Engineering
|
Applying Generalized Linear
Models
by James K. Lindsey, New York: Springer-Verlag New York, Inc., 1997 |
| Supplementary Text: | Generalized Linear Models,
2nd edition
by P. McCullagh and J. A. Nelder, London: Chapman and Hall, 1989 |
 |
S-Plus 2000
/ S-Plus 5.1 |
 |
MRC Biostatistics Unit,Cambridge UK |
This course covers the theory and practice of modern regression modeling within
the unifying framework of the GLM. Topics include: Likelihood-based inference
in generalized linear models (GLMs); Brief review of multiple linear regression,
theory and practice; Theory of GLMs;Discrete models: binary regression and contingency
tables. Introduction to log-linear models. Data analysis: model fitting, model
choice, and residuals-based diagnostics. Elements of sampling theories and Bayesian
theories of inference in GLMs. Routine use of statistical software, for model
exploration and fitting and for data analysis. MCMC methods for Bayesian analysis
in GLMs. Failure-time data (survival analysis); extensions (as time permits)
to include generalized additive models, growth curves and dynamic linear models,
repeated categorical data, frailty models, quasilikelihood and generalized estimating
equations (GEEs) Students will use appropriate computer software for data analysis
and manipulation, model construction, assessment and model-based inference.
Prerequisite: STA 215 and STA 244 or equivalent.
Computing Links| Course
Calendar | ACPUB's
Course Info | Duke Statistics Home
August 4, 2000