Fundamentals of Statistical Inference

3 credits · Graduate credit

This course covers core topics in statistical theory. It starts with a brief review of necessary probability concepts such as types of convergence and background on exponential families and related topics. The core topics include sample and asymptotic variance bounds, maximum likelihood estimation and likelihood ratio theory, asymptotic relative efficiency, the EM algorithm, M-estimation, robustness, multiple testing, fundamentals of decision theory and Bayesian inference, empirical Bayes and Steinian shrinkage.