Online class on Tuesday, April 13, 14h30-17h00

Generalized Linear Models [Slides 1-57] Bibliography. Introduction and motivating example. The 3 components of a GLM. The definition of the (2-parameter) exponential family of distributions: the Normal, Poisson, Bernoulli and 'Binomial/n' as special cases of the exponential family of distributions. Link functions and the concept of a canonical link function. The Linear Model as GLM. The Logistic Regression as a GLM for Bernoulli or Binomial/n random component, with the canonical link. The R command glm and its arguments and auxiliary functions. The Hosmer & Lemeshow example. Properties of the logistic curve and of a Logistic Regression. Estimating parameters in a GLM by Maximum Likelihood: an overview of the problem; the nature of the log-likelihood as a function of the model parameters beta_j; the system of normal equations in a Logistic Regression and the need for numerical algorithms to find the maximum-likelihood estimators.