ST522 - Statistical Theory II
- Prerequisites: ST 521
- Term & Frequency: Every Spring
- Student Audience: PhD students in Statistics and related fields
- Credit: 3 credits
- Recent Texts: Statistical Inference, by Casella and Berger
- Recent Instructors: Howard Bondell, Subhashis Ghoshal, Donald Martin, Jacqueline Hughes-Oliver
- Background and Goals: There are two sections of this course. One is for graduate students in statistics, the other is for students in other disciplines. This course is designed to provide the basic tools of statistical inference to graduate students. It should prepare the students to understand the foundations behind statistical inference, and enable them to formulate appropriate statistical procedures. It should further hone their problem solving skills, as well as prepare them to handle more advanced courses.
- Content: Sufficient, ancillary, and complete statistics; Methods of finding estimators, including maximum likelihood; Mean squared error and unbiasedness; Hypothesis testing, including likelihood ratio; Power functions; Neyman-Pearson Lemma; Uniformly most powerful tests; Confidence intervals; Asymptotic properties of estimators and tests.
- Alternatives: none
- Subsequent Courses: Advanced Inference (ST 793) for Statistics PhD students
SP 2017 Sections:
|001||Hughes-Oliver,J||01108 SAS Hall||08:30AM-09:45AM||MW||30/35 - Open||ST522-001|
|001A||Hughes-Oliver,J||01108 SAS Hall||10:00AM-10:50AM||W||30/35 - Open|