Lectures related to Regression Modeling: Variable Selection

Related lectures (32)

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.