This course on Generalized Linear Models aims to teach learners the background, canonical link functions, likelihood, and Fisher information related to GLMs. Participants will also learn about Probit Regression, Logistic "Logit" Regression, Complementary Log Log Regression, Poisson Regression, Ordinal Logistic Regression, and Multinomial Logistic Regression. The course utilizes an Iteratively Re-weighted Least Squares approach for a general link function. The intended audience for this course includes individuals interested in statistics, data analysis, and regression modeling.
Overview
Syllabus
Generalized Linear Models: Background.
Generalized Linear Models: Canonical Link Function.
Generalized Linear Models: Likelihood, Score, and Fisher Information.
GLM: Iteratively Re-weighted Least Squares for a General Link Function.
Generalized Linear Models: Probit Regression (part 1).
Generalized Linear Models: Probit Regression (part 2).
Generalized Linear Models: Logistic "Logit" Regression (part 1).
Generalized Linear Models: Logistic "Logit" Regression (part 2).
Generalized Linear Models: Logistic "Logit" Regression (part 2).
Generalized Linear Models: Complementary Log Log Regression (part 1).
Generalized Linear Models: Complementary Log Log Regression (part 2).
Generalized Linear Models: Complementary Log Log Regression (part 2).
Generalized Linear Models: Poisson Regression with Canonical Link (part 1).
Generalized Linear Models: Poisson Regression with Canonical Link (part 2).
Ordinal Logistic Regression (Proportional Odds Model).
Multinomial Logistic Regression.
Taught by
statisticsmatt
