STA215: Statistical Inference

Prof:Robert L. Wolpert wolpert@stat.duke.edu (684-3275)
Class:Tue & Thu 2:15-3:30pm Old Chemistry 025
OH:Mon 3:45-5:00pm
Fri 2:15-3:15pm		 Old Chemistry 211c
Background:George Casella & Roger Berger, Statistical Inference
Optional:James Berger & Robert Wolpert, The Likelihood Principle (2nd edn)
Helio Migon & Dani Gamerman, Statistical Inference: An Integrated Approach
Phil Spector, An Introduction to S and S-Plus
Required: Peter Bickel & Kjell Doksum, Mathematical Statistics: Basic Ideas and Selected Topics

Description

This is a course about making inference using statistics, or functions of observed data: this includes the (point and interval) estimation of uncertain parameters and the testing of statistical hypotheses. All three contemporary paradigms of inference (Likelihood, Classical, Bayesian) are presented; traditional properties of estimators (bias, consistency, efficiency, sufficiency, etc.) and tests (size, power, probability) are considered in detail.

Students are assumed to be familiar with random variables and their distributions from a calculus-based or measure-theoretic introduction to probability theory. Some problems and projects will require computation; students should be or become familiar with either S-Plus (some notes and an intro are available, also in an older but nice form (Contents, 1-29, 30-64, 65-85, Examples), as well as the optional text by Spector listed above) or Matlab (a primer and intro are available), both easier to use than compiled languages like f77, c, or c++.

Not all homework sets will be graded, but they should be turned in for comment; Tuesday classes will begin with a class solution of two of the preceeding week's problems. Here is at least a tentative schedule, containing most of the topics below.

OUTLINE -- course topics will include: (look here for a tentative schedule)