STA215: Statistical Inference

 Prof: Robert L. Wolpert wolpert@stat.duke.edu (211c Old Chem, 684-3275) TA: Merrill Liechty merrill@stat.duke.edu (222 Old Chem, 684-8088) Class: Tue & Thu 2:15-3:30pm Old Chemistry 025 OH: Mon 3:45-5:00pmFri 2:15-3:15pm Old Chemistry 211c Text: Peter Bickel & Kjell Doksum, Mathematical Statistics: Basic Ideas and Selected Topics (2nd edn) Opt'l: James Berger & Robert Wolpert, The Likelihood Principle (2nd edn) George Casella & Roger Berger, Statistical Inference Andrew Gelman, John Carlin, Hal Stern, & Don Rubin, Bayesian Data Analysis John Kalbfleisch & Ross Prentice, The Statistical Analysis of Failure Time Data Erich Lehmann, Theory of Point Estimation and Testing Statistical Hypotheses Tom Leonard & John Hsu, Bayesian Methods Anthony O'Hagan, Kendall's Advanced Theory of Statistics, v2B: Bayesian Inference Comp: Phil Spector, An Introduction to S and S-Plus

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)

• Review of Probability (e.g. dist'ns)
• Likelihood Functions (notes)
• Likelihoodist, Bayes, & Frequentist (incl. Classical) Paradigms
• Exponential Families
• Sufficiency
• Observed & Expected (Fisher) Information
• Nuisance Parameters
• The Likelihood Principal
• Point & Interval Estimation
• Consistency
• Confidence & Credible Intervals
• Coverage Probability
• Frêchet (Cramér-Rao) Lower Bound
• Efficiency & Robustness
• Testing Statistical Hypotheses
• P-Values
• Posterior Probabilities
• Size & Power
• Neyman-Pearson Lemma