Lectures related to Derivation of the logit model

Mixtures: introduction MOOC: Selected Topics on Discrete Choice

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

Discrete Choice Analysis

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

Red bus/Blue bus paradox

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

Mixture Models: Simulation-based Estimation

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

Choice Models: Logit Model Derivation

Covers the derivation of the logit model for choice with multiple alternatives and the challenges in generating choice sets.

Derivation of the logit model MOOC: Introduction to Discrete Choice Models

Delves into the logit model's derivation, emphasizing the importance of the independence assumption and parameter normalization during estimation.

Deterministic Part: Utility Function Coding MOOC: Introduction to Discrete Choice Models

Explores coding behavioral assumptions into a linear-in-parameter utility function.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Mixture models: alternative specific variance MOOC: Selected Topics on Discrete Choice

Explores alternative specific variance in mixture models and discusses identification issues and model comparisons using 500 draws.

Continuous Random Variables

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Continuous Random Variables: Basics

Explores continuous random variables, cdf, pdf, convolution, and sum of dice.

Calculations of Expectation

Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.

Random Variables and Expected Value

Introduces random variables, probability distributions, and expected values through practical examples.

Linear Regression: Theory and Applications

Covers the theory and practical applications of linear regression.

Generalized Linear Regression: Classification

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

Probability and Statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Fundamental Limits of Gradient-Based Learning

Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.

Model Specification: The Error Term MOOC: Introduction to Discrete Choice Models

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

Sampling: conditional maximum likelihood estimation MOOC: Selected Topics on Discrete Choice

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

The red bud-blue bus paradox MOOC: Selected Topics on Discrete Choice

Discusses the red bud-blue bus paradox in mode choice modeling and its policy implications.