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STAT 710

MATHEMATICAL STATISTICS II

4 credits.

Estimation theory, efficiency, Neyman-Pearson theory of hypothesis testing, confidence regions, decision theory, likelihood ratio theory, current research topics in mathematical statistics.

Prerequisites & course information

Requisites: STAT/MATH 709 Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Define and distinguish different notions of optimality in point estimation, hypothesis testing, and confidence sets. Audience: Graduate 2. Apply standard techniques to derive optimal estimators, hypothesis tests, and confidence sets. Audience: Graduate 3. Study statistical inference procedures for parametric and non-parametric models. Audience: Graduate 4. Investigate asymptotic analysis of point estimators, hypothesis tests, and confidence sets. Audience: Graduate 5. Understand and critique current research in mathematical statistics. Audience: Graduate Learning Outcomes: 1. Define and distinguish different notions of optimality in point estimation, hypothesis testing, and confidence sets. Audience: Graduate 2. Apply standard techniques to derive optimal estimators, hypothesis tests, and confidence sets. Audience: Graduate 3. Study statistical inference procedures for parametric and non-parametric models. Audience: Graduate 4. Investigate asymptotic analysis of point estimators, hypothesis tests, and confidence sets. Audience: Graduate 5. Understand and critique current research in mathematical statistics. Audience: Graduate

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