| Course Description |
This course provides a general introduction to mathematical statistics and statistical decision theory for doctoral students in biostatistics. It covers elementary decision theory, Bayes rules, Neyman-Pearson theory, uniformly most power tests, similar tests, uniformly most powerful unbiased tests, confidence sets; basic asymptotic criteria, estimation methods and their asymptotic properties, M-estimators, U-statistics, statistical functionals; likelihood ratio tests, Wald tests, Rao's score tests, and their asymptotic properties, Pitman efficiency. This course will prepare students to their theory qualifying exam.
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