Explores special examples of Generalized Linear Models, covering logistic regression, count data models, separation issues, and nonparametric relationships.
Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.
Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.
Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.