Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Delves into the logit model's derivation, emphasizing the importance of the independence assumption and parameter normalization during estimation.
Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Explains the derivation of the logit model in choice models, covering error terms, choice sets, and availability conditions.
Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.
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 linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
