ST702 - Statistical Inference II
- Prerequisites: ST 701
- 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: Subhashis Ghoshal, Donald Martin, Jacqueline Hughes-Oliver
- Background and Goals: 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
S1 2017 Sections:
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