Transcript STA 216 Generalized Linear Models Instructor: David Dunson

STA 216
Generalized Linear
Models
Instructor: David Dunson
[email protected]
211 Old Chem, 541-3033 (NIEHS)
STA 216 Syllabus
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Topics to be covered:
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Definition of GLM: Components, assumptions and
motivating examples
The Basics: Exponential family, model fitting, and
analysis of deviance
Binary Data (Models): Link functions, parameter
interpretation, & prior specification
Binary Data (Computation): Approximations and
MCMC algorithms
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Topics (Page 2)
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Binary Data (Probit Models): Underlying normal
structure and Albert & Chib Gibbs sampler
Ordered Categorical Data: Probit models, common
link functions, and examples
Unordered Categorical Data: Multinomial choice
models, common link functions and examples
Log-Linear Models: Poisson distribution, parameter
interpretation, over-dispersion and examples
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Topics (Page 3)
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Discrete-Time Survival Models: Relationship with
binary data models, convenient forms & examples
Continuous-Time Survival: Proportional hazards
model, counting processes & implementation
Accounting for Dependency: Mixed models for
longitudinal and multilevel data
Multivariate GLMs: Generalized linear mixed models
for multivariate response data
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Topics (Page 4)
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Models for Mixed Discrete & Continuous Outcomes:
Underlying normal & GLMM approaches
Advanced Topics:
Incorporating parameter constraints
 Hidden Markov and multi-state modeling
 Case Studies: Fertility and tumorigenicity applications
 Non- and semi-parametric methods
 Identifiability & improved methods for computation
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Student Responsibilities:
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Assignments: Outside reading and problems sets
will typically be assigned after each class (10%)
Mid-term Examination: An in-class closed-book mid
term examination will be given (30%)
Project: Students will be expected to write-up and
present results from a data analysis project (30%)
Final Examination: The final examination will have
both in-class (15%) & out of class problems (15%)