Dive into a comprehensive 3.5-hour tutorial on Generalized Linear Models (GLMs). Explore the background, canonical link functions, and key concepts like likelihood, score, and Fisher information. Master iteratively re-weighted least squares for general link functions. Delve into specific GLM types including probit, logistic (logit), complementary log-log, and Poisson regression. Gain insights into ordinal logistic regression using the proportional odds model and multinomial logistic regression. Enhance your statistical modeling skills with in-depth explanations and practical applications of these powerful techniques.
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
