Ordered logit

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Generalized Linear Models: GLMs for Non-Gaussian Data

Explores Generalized Linear Models for non-Gaussian data, covering interpretation of natural link function, MLE asymptotic normality, deviance measures, residuals, and logistic regression.

Panel data: static model

Introduces the static model for panel data analysis and discusses its limitations.

Logistic Regression: Part 1

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

Mixtures: introduction

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

Population Growth and Resource Consumption

Explores population growth models, resource consumption, and real-world case studies.

Logistic Regression: Interpretation & Feature Engineering

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Linear Models: Recap and Logistic Regression

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Sampling: conditional maximum likelihood estimation

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

Linear Models for Classification: Multi-Class Extensions

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Mixture models: summary

Summarizes mixtures of logit models, covering various mixing methods and modeling techniques for taste heterogeneity.