Discrete Choice Models: Selected Topics

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 3 of 4

Binary Choice Model

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

Model Specification: The Error Term

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

Extreme Value Models: Technical Derivation

Explores the technical derivation and properties of Multivariate Extreme Value models.

The Nested Logit Model

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

Weighted Least Squares Estimation: IRLS Algorithm

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

Logistic Regression: Part 1

Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.

Logistic Regression: Probabilistic Interpretation

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Horseshoe Crabs: Logistic Regression Analysis

Explores logistic regression analysis of horseshoe crab data, focusing on odds ratio interpretation and model fitting.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Generalized Linear Models: Examples and Applications

Explores special examples of Generalized Linear Models, covering logistic regression, count data models, separation issues, and nonparametric relationships.