Estimation, Shrinkage and Penalization

This lecture, given by the instructor on November 16, 2020, delves into the concepts of estimation, shrinkage, and penalization in statistics for data science. The lecture covers topics such as the unique minimizer of a specific function, the role of shrinkage in reducing coefficient size, and the implications of different types of norms in regression. The instructor explains the importance of balancing bias and variance in statistical estimation, showcasing how different techniques like ridge regression and LASSO can improve model performance. The lecture concludes with discussions on the generalized linear model, model selection, and the asymptotic normality of maximum likelihood estimators in GLMs.