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Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.
Introduces mixtures, covers discrete and continuous mixtures, explores examples, and discusses combining probit and logit models.
Covers iterative weighted least squares, model checking, Poisson regression, and fitting multinomial models using Poisson errors.
