Explores the nested logit model for discrete choice and its implications on choice behavior and parameter estimation.
Introduces mixtures, covers discrete and continuous mixtures, explores examples, and discusses combining probit and logit models.
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.
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.
