Lecture
Generalized Linear Models: Examples and Applications
Related lectures (49)
Weighted Least Squares Estimation: IRLS Algorithm
Explores the IRLS algorithm for weighted least squares estimation in GLM.
Generalized Linear Models
Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.
Generalized Linear Regression: Classification
Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.
Logistic Regression: Vegetation Prediction
Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.
Generalized Linear Models: A Brief Review
Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.
Red bus/Blue bus paradox
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Generalized Linear Models II: GLM Extensions
Covers advanced topics in Generalized Linear Models, focusing on link functions, error distributions, and model interpretation.
Mixture Models: Simulation-based Estimation
Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.
Model Selection Criteria: AIC, BIC, Cp
Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.
MLE Applications: Binary Choice Models
Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.