Course: MATH 541B, Graduate Mathematical Statistics II, Fall 2025
Prerequisite: Math 541A
Course Content: Hypotheses testing, Neyman-Pearson lemma, generalized likelihood ratio procedures, confidence intervals, consistency, power, jackknife and bootstrap. Monte Carlo Markov chain methods, hidden Markov models.
Last update: 27 May 2025

Instructor: Steven Heilman, stevenmheilman(@-symbol)gmail.com
Office Hours: Mondays 12PM-1PM, Fridays 1015AM-11AM, KAP 406G
Lecture Meeting Time/Location: Mondays, Wednesdays and Fridays 11AM-1150AM, KAP 140
TA:
TA Office Hours: (see the schedule in the Math Center)
Recommended Textbook: Cassella and Berger, Statistical Inference, 2nd Edition. 
Other Textbooks (not required): Lehmann and Romano, Testing Statistical Hypotheses
Ferguson, A Course in Large Sample Theory.
Shao and Tu, The jackknife and bootstrap.
McLachlan and Krishnan, The EM Algorithm and Extensions.
Robert and Casella, Monte Carlo and Statistical Applications.
Haggstrom, Finite Markov Chains and Algorithmic Applications.
Levin and Peres, Markov Chains and Mixing Times.

Midterm 1: Friday, October 3, 11AM-1150AM KAP 140
Midterm 2: Monday, November 3 , 11AM-1150AM KAP 140
Final Exam: Wednesday, December 10, 11AM-1PM, KAP 140
Other Resources: An introduction to mathematical arguments, Michael Hutchings, An Introduction to Proofs, How to Write Mathematical Arguments
Email Policy:

Exam Resources: Here are the exams from the last time I taught this class: Exam 1 Exam 1 Solution Exam 2 Exam 2 Solution Final Final Solution.
Here is a page with some practice exams (at the bottom). Here is a page with some practice exams (see Assignment 3). Here is a page containing USC Stats B Qual Exams with solutions. Here is another page with some related Stats qualifying exams (see e.g. the Theory exams).

Exam Procedures: Students must bring their USCID cards to the midterms and to the final exam. Phones must be turned off. Cheating on an exam results in a score of zero on that exam. Exams can be regraded at most 15 days after the date of the exam. This policy extends to homeworks as well. All students are expected to be familiar with the USC Student Conduct Code. (See also here.)
Accessibility Services: If you are registered with accessibility services, I would be happy to discuss this at the beginning of the course. Any student requesting accommodations based on a disability is required to register with Accessibility Services (OSAS) each semester. A letter of verification for approved accommodations can be obtained from OSAS. Please be sure the letter is delivered to me as early in the semester as possible. OSAS is located in 301 STU and is open 8:30am-5:00pm, Monday through Friday.
https://osas.usc.edu/
213-740-0776 (phone)
213-740-6948 (TDD only)
213-740-8216 (fax)
OSASFrontDesk@usc.edu

Discrimination, sexual assault, and harassment are not tolerated by the university. You are encouraged to report any incidents to the Office of Equity and Diversity http://equity.usc.edu/ or to the Department of Public Safety http://capsnet.usc.edu/department/department-public-safety/online-forms/contact-us. This is important for the safety whole USC community. Another member of the university community - such as a friend, classmate, advisor, or faculty member - can help initiate the report, or can initiate the report on behalf of another person. The Center for Women and Men http://www.usc.edu/student-affairs/cwm/ provides 24/7 confidential support, and the sexual assault resource center webpage sarc@usc.edu describes reporting options and other resources.

Homework Policy:

Grading Policy:

Tentative Schedule: (This schedule may change slightly during the course.)

Week Monday Tuesday Wednesday Thur Friday
1 Aug 25: 8.1, Hypothesis Testing Aug 26 Aug 27: 8.1, Hypothesis Testing Aug 28 Aug 29: 8.2, Likelihood Ratio Tests
2 Sep 1: No class Sep 2 Sep 3: 8.3.1, Neyman-Pearson Lemma Sep 4 Sep 5: Homework 1 due. 8.3.2, Karlin-Rubin Theorem
3 Sep 8: 8.3, Exponential Families Sep 9 Sep 10: 8.3, Unbiasedness Sep 11 Sep 12: 8.3.4, p-values
4 Sep 15: 8.3.5, Loss Function Optimality Sep 16 Sep 17: 9.1, Confidence Intervals Sep 18 Sep 19: Homework 2 due, 9.2, Test Inversion
5 Sep 22: 9.2.2, Pivotal Method Sep 23 Sep 24: 9.2.3, Pivoting CDF Sep 25 Sep 26: 9.2.4, Bayesian Intervals
6 Sep 29: 9.3.4, Loss Function and CI Sep 30 Oct 1: 10.3, Asymptotics of Likelihood Ratio Oct 2 Oct 3: Exam 1
7 Oct 6: 10.3, Asymptotics of GLR Oct 7 Oct 8: 10.3, Asymptotics of GLR Oct 9 Oct 10: No class. Homework 3 due
8 Oct 13: Jackknife Oct 14 Oct 15: Jackknife Oct 16 Oct 17: Bootstrap
9 Oct 20: Bootstrap Oct 21 Oct 22: Consistency of Jackknife Oct 23 Oct 24: Homework 4 due, Bootstrap/Jackknife Relationship
10 Oct 27: EM Algorithm Oct 28 Oct 29: EM Algorithm Oct 30 Oct 31: EM Algorithm
11 Nov 3: Exam 2 Nov 4 Nov 5: EM Algorithm Convergence Nov 6 Nov 7: Homework 5 due. EM Algorithm Convergence
12 Nov 10: Monte Carlo Nov 11: Nov 12: Rejection Sampling Nov 13 Nov 14: Rejection Sampling
13 Nov 17: Importance Sampling Nov 18 Nov 19: Markov Chains Nov 20 Nov 21: Homework 6 due, Markov Chains
14 Nov 24: Metropolis-Hastings Nov 25 Nov 26: No class Nov 27 Nov 28: No class
15 Dec 1: Markov Chain Monte Carlo Dec 2 Dec 3: Review of Course Dec 4 Dec 5: Homework 7 due, Review of Course

Advice on succeeding in a math class:

Homework

Homework .tex files

Supplementary Notes