| 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 3:45-5:00pm Fri 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,
v | ||
| Comp: | Phil Spector, | An Introduction to S and S-Plus |
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)