Math5062: Theory of Statistics II (Spring 2007)

 

Instructor:

Nan Lin

Office:

Cupples I, Room 205

Phone:

935-5703

Email:

 

Time and location:

2:00pm - 3:00pm MWF at Cupples I, Room 216

Office hours:

By appointment. You are also encouraged to email me your questions. You will receive my reply within 24 hours with probability 0.95.

Textbook:

Bickel, P. J. and Doksum, K. A. (2001) Mathematical Statistics: Basic Ideas and Selected Topics (2nd Edition), Prentice Hall.

Lehmann, E. L. (2005) Testing Statistical Hypotheses, 2nd edition, Springer.

Prerequisites:

You should be familiar with the following topics in probability and mathematical statistics theory. Convergence in probability; convergence in distribution; law of large numbers; central limit theorem; slutzky theorem; the delta method; asymptotic normality of the MLE. If you have questions about the prerequisites, please contact the instructor.

Course description:

This course intends to provide rigorous training in mathematical statistics for graduate students with sufficient background. We will focus on the classical mathematical statistics theory of hypothesis testing, interval estimation and some nonparametric techniques. The topics include the Neyman-Pearson framework of hypothesis testing, large sample tests, nonparametric tests, resampling methods and Bayesian methods.

Syllabus

 

Schedule

 

Week 1

Introduction, likelihood-ratio test

Homework 1

 

Week 2

Neyman-Pearson lemma, p-value, UMP tests, Monotone likelihood ratio

Homework 2

 

Week 3

Generalized Neyman-Pearson lemma, UMP tests for two-sided hypotheses, least favorable distribution

 

 

Week 4

Unbiased tests, UMPU tests in exponential family

Homework 3

 

Week 5

Applications of the UMPU test theory

Homework 4

 

Week 6

More applications of UMPU test theory, Wald test, score test

 

 

Week 7

Maximum likelihood ratio test

 

 

Week 8

Theory of asymptotic tests

Homework 5

 

Week 9

Chi-square tests to categorical data, Bayes tests

 

 

Week 10

Nonparametric tests, U-statistics,

 

 

Week 11

V-statistics, Kolmogorov test,

 

 

Week 12

Cramer-von Mises test

Homework 6

 

Week 13

Shapiro-Wilks tests,

 

 

Week 14

Pivotal quantity, Inverting acceptance regions of tests, UMA confidence interval, Credible interval, prediction interval

Homework 7

 

Week 15

Bootstrap method, multiple testing