Mixture models: alternative specific variance

Related lectures (31)

Page 2 of 4

Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.

Covers Conditional Maximum Likelihood estimation, contribution to likelihood, and MEV model application in choice-based samples.

Introduces mixtures, covers discrete and continuous mixtures, explores examples, and discusses combining probit and logit models.

Explores common probability distributions, special distributions, and entropy concepts.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Delves into the binary choice model, error term specification, and Extreme Value distribution properties.

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores the nested logit model for discrete choice and its implications on choice behavior and parameter estimation.

Explores the IRLS algorithm for weighted least squares estimation in GLM.