Generalized Linear Models
Generalized linear models framework, binary data, polytomous data, log-linear models.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesSTAT 535 & STAT 642
Course Outcomes: 

Write a GLM

For any exponential family of distributions, write a GLM in the random/link/systematic component framework.

Identify the canonical link

Determine the canonical link for any distribution in the exponential family.

Fit GLM using software

Fit (using frequentist and Bayesian methods) and choose an appropriate generalized linear model for binary, ordered categorical, unordered categorical, and count response variables using R, SAS, and WinBUGS/OpenBUGS/JAGS.

Mathematically solve and compute the MLE's for coefficients of any basic GLM

Reproduce (for any distribution in the exponential family) score equations, Fisher information, and write out the form of the iterative reweighted least squares algorithm for finding maximum likelihood estimates of the coefficients.

Evaluate a fitted GLM

Evaluate the validity/appropriateness of the chosen model using model diagnostics such as residual plots and deviance.

Identify model weaknesses and strengths

Identify weaknesses and strengths in the chosen model for a given data set.

Predict and provide confidence intervals

Make predictions and determine confidence intervals using the fitted model.

Identify and account for overdispersion

Identify when overdispersion is present in a given data set and ways to account for overdispersion in the model.

Fit and interpret output from a non-standard GLM

Fit using R and/or SAS and interpret the output from a generalized linear mixed model, zero-inflated model, gamma regression model, and GLM's for dependent data.