Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Explains the derivation of the logit model in choice models, covering error terms, choice sets, and availability conditions.
Delves into the logit model's derivation, emphasizing the importance of the independence assumption and parameter normalization during estimation.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Introduces mixtures, covers discrete and continuous mixtures, explores examples, and discusses combining probit and logit models.
Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.
Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.
Summarizes mixtures of logit models, covering various mixing methods and modeling techniques for taste heterogeneity.
