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Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Explores the nested logit model for discrete choice and its implications on choice behavior and parameter estimation.
Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.
Explains the derivation of the logit model in choice models, covering error terms, choice sets, and availability conditions.
Introduces mixtures, covers discrete and continuous mixtures, explores examples, and discusses combining probit and logit models.
Delves into the logit model's derivation, emphasizing the importance of the independence assumption and parameter normalization during estimation.
