Estimators and Bias

This lecture covers the concept of estimators in statistics, focusing on M-estimators and their properties such as bias and mean squared error. It explains how bias and variance affect the accuracy of estimators, with examples illustrating the trade-off between bias and variability. The lecture also discusses the importance of unbiased estimators and efficiency in estimation, particularly in the presence of outliers. Additionally, it explores the maximum likelihood method and the method of moments for parameter estimation, highlighting the concepts of consistency and robustness in statistical inference.