Sampling Distributions: Theory and Applications

This lecture by the instructor covers the theory and applications of sampling distributions, focusing on topics such as minimal sufficiency, Gaussian sufficient statistics, t-distributions, Fisher F distribution, convergence in distribution, and the Delta method. The lecture delves into the properties of estimators, including consistency, identifiability, mean square error, and measures of performance. Various theorems and definitions related to sampling distributions and estimation are discussed, providing a comprehensive overview of statistical concepts essential for data science.