The Nested Logit Model

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Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

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

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Covers linear models for classification, focusing on logistic regression and support vector machines.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.