1 / 6

STA 216 Generalized Linear Models

Dive into the fundamentals of GLMs, from binary and categorical data to survival and mixed models. Learn theory, application, and computation methods. Includes case studies and advanced topics. Take on assignments, exams, and projects to enhance understanding.

sconley
Download Presentation

STA 216 Generalized Linear Models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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%)

More Related