Generalized Linear Models: Course Outline
In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution.
E-Books (Full Text)
- Logit model for dichotomous data with single and multiple explanatory variables ,
- maximum likelihood (ML) estimation,
- large sample tests about parameters ,
- good of fit ,
- analysis of deviance ,
- variable selection ,
- extension to polytomous data ,
- introduction to Poisson regression.
- Linear models for two and three dimensional contingency tables:
- interpretation of parameters,
- comparison with ANOVA and regression.
- ML estimation of parameters.
- Likelihood ratio tests for various hypotheses,
- including independence,
- marginal and conditional independence,
- partial association .
- Models with quantitative levels.
- Nonparametric regression and generalized linear models.
- Interpolating and smoothing splines for simple regression.
- Use of cross-validation Applications to Logistic and Poisson regression.
- Additive models and generalized additive models.