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College of Arts & Sciences
Department of Statistics


STAT 823

823-Large Sample Theory. (3) (Prerequisite: STAT 713) Modes of convergence, limit theorems, and the asymptotic properties of estimators and tests.

Usually Offered: Spring even years.

Purpose: The purpose of this course is to introduce students to the theory and application of large-sample methods. The emphasis of this course will be on applications, focusing largely on understanding and formulating large-sample arguments.

Current Textbook: Ferguson, T. A Course in Large Sample Theory. New York: Chapman & Hall, 1996.

 

Topics Covered Time
Review of analysis and probability theory: 1.5 weeks
Modes of convergence: 2 weeks 2 weeks
Central Limit Theorem (iid case), Approximation by averages: 2 weeks
Delta Method, Slutsky's Theorem: 1.5 weeks
Central Limit Theorem (non-iid case), bootstrap, Edgeworth expansions: 1.5 weeks
Maximum likelihood estimation: 2 weeks
Asymptotically optimal hypothesis tests: 1.5 weeks
M-estimation: 2 weeks

The above textbook and course outline should correspond to the most recent offering of the course by the Statistics Department. Please check the current course homepage or with the instructor for the course regulations, expectations, and operating procedures.

Contact Faculty: Joshua Tebbs