Generalized Linear Models: Theory and Applications

This lecture by the instructor covers the theory and applications of Generalized Linear Models (GLM). It explains the asymptotic normality of Maximum Likelihood Estimation (MLE) in GLM, the conditions required for convergence, and the interpretation of the results. The lecture also discusses the measures of fit in GLM, the role of shrinkage in model estimation, and the importance of choosing the appropriate link functions. Special examples such as Logistic Regression for Binary Data and Loglinear Regression for Count Data are presented to illustrate the practical applications of GLM.