Point Estimation in Statistics

This lecture covers the concept of point estimation in statistics, focusing on the properties of point estimators, such as bias and variance. The instructor explains the trade-off between bias and variance, the mean squared error, and the consistency of estimators. The lecture also delves into the Cromerow lower bound, which sets a limit on the variance of unbiased estimators. Through examples and mathematical derivations, the lecture illustrates how to compare estimators using the mean squared error and discusses the challenges of identifiability in parameter estimation. The importance of asymptotic properties, such as consistency and convergence, is emphasized to understand the behavior of estimators as sample size increases.