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STA 216 Generalized Linear Models

STA 216 Generalized Linear Models. Instructor: David Dunson dunson1@niehs.nih.gov 211 Old Chem, 541-3033 (NIEHS). STA 216 Syllabus. Topics to be covered: Definition of GLM : Components, assumptions and motivating examples

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STA 216 Generalized Linear Models

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  1. STA 216Generalized Linear Models Instructor: David Dunson dunson1@niehs.nih.gov 211 Old Chem, 541-3033 (NIEHS)

  2. STA 216 Syllabus • Topics to be covered: • 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

  3. Topics (Page 2) • 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

  4. Topics (Page 3) • 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

  5. Topics (Page 4) • 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

  6. Student Responsibilities: • 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%)

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