Prof: | Robert L. Wolpert |
wolpert@stat.duke.edu (684-3275) | |

Office Hours: | Old Chem 211c, Mon 4:15-5:00pm or by appointment | ||

TA: | Zhenglei Gao |
zhenglei@stat.duke.edu (684-9390) | |

Office Hours: | Old Chem 214D, Tue 4-6pm or by appointment | ||

Class: | Mon & Wed 1:15-2:30pm | Old Chem 025 | |

Text: | Peter Bickel & Kjell Doksum, | Mathematical Statistics: Basic Ideas and Selected Topics (2nd edn) | |

Recc: | George Casella & Roger Berger, | Statistical Inference (Chaps 3.3, 6-9) | |

Larry Wasserman, | All of Statistics (Chaps 6,7,9-12) | ||

Opt'l: | James Berger & Robert Wolpert, | The Likelihood Principle (2nd edn) | |

Andrew Gelman, John Carlin, Hal Stern, & Don Rubin, |
Bayesian Data Analysis | ||

Erich Lehmann, | Theory of Point Estimation and Testing Statistical Hypotheses |

Home Page | Syllabus | Computing | ACES |

Students are assumed to be familiar with random variables and their
distributions from a calculus-based introduction to probability theory, at
the level of the first five chapters of *Statistical Inference* by
Casella & Berger or *Probability and Statistics* by DeGroot, or the
first nine chapters of *A First Course in Probability* by Ross. Some
problems and projects will require computation; students should be or become
familiar with either `R` (some notes and an intro are available, also in an
older but nice form (Contents, 1-29, 30-64, 65-85, Examples)) or `Matlab` (a primer and intro are available), both easier to
use than compiled languages like `FORTRAN` or `C`.

Not all homework sets will be graded, but they should be turned in for comment; Monday classes will often begin with a class solution of one or 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

My rules about auditors are that a student can sit in on or (preferably) audit a course if:

- There are enough seats in the room,
- He/she is willing to commit to active participation:
- turn in about a third or a half of the homeworks (or a few problems on each of most HW assignments)
- take either the final or the midterm
- come regularly to lectures, and ask or answer questions now and then.