This course takes a more theoretical look at estimation and hypothesis testing than Mathematics 254 (Statistical Models). Topics include maximum likelihood estimators (MLE’s), the information inequality, asymptotic theory of MLE’s, complete sufficient statistics, uniformly minimum variance unbiased estimators, likelihood ratio tests, most powerful tests, uniformly most powerful tests, and Bayesian statistics. This course is offered in the spring semester on an irregular basis.
Prerequisite: Mathematics 253 (Probability Models) and Mathematics 254 (Statistical Models)
