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.
Summarizes mixtures of logit models, covering various mixing methods and modeling techniques for taste heterogeneity.
Delves into the logit model's derivation, emphasizing the importance of the independence assumption and parameter normalization during estimation.
Explores convex optimization, convex functions, and their properties, including strict convexity and strong convexity, as well as different types of convex functions like linear affine functions and norms.
