Math 728 - Statistical Theory

Spring 2019

TR 11:00--12:15 SNOW 256

Terry Soo, Snow 507

Office hours: Tuesdays 2-3. Wednesdays 2-3

Course description.

In this course we will cover core topics in point estimation, hypothesis testing, and Bayesian statistics. Highlights include the Cramer-Rao bound, the Rao-Blackwell theorem, the Lehmann-Scheffe theorem and the Neyman-Pearson lemma. We will also cover additional topics such as the EM algorithm depending on class interests and background. We may also learn to basics of R, depending on class interests.

Proofs will be an important part of the course. Students should be comfortable with reading and writing proofs. This is a graduate course. This course will help mathematics graduate students who are preparing for the Probability and Statistics qualifying examination, and requires a high level of mathematical maturity. This course will provide a firm foundation for the further study of statistics.

Prerequisites.

Math 727. In particular, students should be comfortable with conditional expectation, the law of large numbers, and the central limit theorem. You should be able to do these sorts of problems: Review Problems-2017

Review Problems-2016

Grading

TBA: Subject to revision

KU final exam schedule

No textbook is required.

Lecture notes will be provided. Extensive course materials are also available from the previous times I have taught the course:

Math 728 2017

Math 728 2016

Notes:

Please see previous years. I may add new material depending on class interest.

Additional Exercises