Basic Principles of Point Estimation

This lecture covers the basic principles of point estimation, focusing on the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle. It explains how estimators are constructed, the Bias-Variance decomposition of Mean Squared Error, and the importance of consistency in estimators. The lecture also delves into the Likelihood Function, Maximum Likelihood Estimators, and the Moment Principle. Examples are provided to illustrate concepts such as Density Estimation, Bias, and Mean Squared Error. The instructor emphasizes the importance of understanding the quality of estimators and the tradeoffs involved in choosing different estimation methods.