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.
Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.
Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.
Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.
Covers simple and multiple linear regression, including least squares estimation and model diagnostics.
Covers heteroscedasticity in nonlinear specifications, modeling scale parameters across different groups in the population.
Covers the Metropolis-Hastings algorithm and gradient-based approaches for biasing searches towards higher likelihood values.
Explores heteroskedasticity in econometrics, discussing its impact on standard errors, alternative estimators, testing methods, and implications for hypothesis testing.
Explores the Frequency Domain study in COMSOL for analyzing acoustic responses to harmonic excitation in various fields.
