Heteroskedasticity: Ch. 4a

Related lectures (32)

Page 3 of 4

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

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

Explores the distribution theory of least squares estimators in a Gaussian linear model, focusing on precision and confidence intervals construction.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Explains least squares approximation for finding best fit lines or curves to data points.

Introduces regression analysis for multivariate data modeling, covering matrix algebra, interpretation of coefficients, and test intervals.

Covers linear models like Ridge, OLS, and LASSO, explaining singular values and regression analysis.

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Covers instrumental variables, addressing endogeneity issues in regression analysis through estimation techniques and practical examples.