Online class on Tuesday, April 20, from 14h30 to 17h00.

[GLM Slides 58-113] A few words on numerical algorithms for parameter estimation in GLMs: the Newton-Raphson type algorithms. Further examples of GLMs for Bernoulli/Binomial response variables: the Probit model (definition, origins in toxicology, the use of a Normal cdf); the complementary log-log model (definition, the use of the Gumbel cdf). Applications with R to the Hosmer & Lemeshow data set. Asymptotic inference for Maximum Likelihood estimators: the general results and their application for GLMs. Asymptotic confidence intervals and hypothesis test for any linear combination of the model parameters. The profiling alternative for confidence intervals. Examples in R. GLMs for Poisson response variables: the canonical link and log-linear models. Exercise GLM 5. Measuring the goodness-of-fit: the deviance (definition, interpretation, the concept of a saturated model and expressions for Poisson, Bernoulli ansd 'Binomial/n' response variables). Comparing models and submodels: Wilk's Theorem and the Likelihood Ratio test. The goodness-of-fit test in Exercise 5.