Course: MATH 541B, Graduate Mathematical Statistics II, Fall 2026
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 2026
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, SSL 202
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 2, 11AM-1150AM SSL 202
Midterm 2: Monday, November 2 , 11AM-1150AM SSL 202
Final Exam: Wednesday, December 9, 11AM-1PM, SSL 202
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.
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 24: 8.1, Hypothesis Testing | Aug 25 | Aug 26: 8.1, Hypothesis Testing | Aug 27 | Aug 28: 8.2, Likelihood Ratio Tests |
| 2 | Aug 31: 8.3.1, Neyman-Pearson Lemma | Sep 1 | Sep 2: 8.3.2, Karlin-Rubin Theorem | Sep 3 | Sep 4: Homework 1 due, 8.3, Exponential Families |
| 3 | Sep 7: No class | Sep 8 | Sep 9: 8.3, Unbiasedness | Sep 10 | Sep 11: 8.3.4, p-values |
| 4 | Sep 14: 8.3.5, Loss Function Optimality | Sep 15 | Sep 16: 9.1, Confidence Intervals | Sep 17 | Sep 18: Homework 2 due, 9.2, Test Inversion |
| 5 | Sep 21: 9.2.2, Pivotal Method | Sep 22 | Sep 23: 9.2.3, Pivoting CDF | Sep 24 | Sep 25: 9.2.4, Bayesian Intervals |
| 6 | Sep 28: 9.3.4, Loss Function and CI | Sep 29 | Sep 30: 10.3, Asymptotics of Likelihood Ratio | Oct 1 | Oct 2: Exam 1 |
| 7 | Oct 5: 10.3, Asymptotics of GLR | Oct 6 | Oct 7: 10.3, Asymptotics of GLR | Oct 8 | Oct 9: No class. Homework 3 due |
| 8 | Oct 12: Jackknife | Oct 13 | Oct 14: Jackknife | Oct 15 | Oct 16: Bootstrap |
| 9 | Oct 19: Bootstrap | Oct 20 | Oct 21: Consistency of Jackknife | Oct 22 | Oct 23: Homework 4 due, Bootstrap/Jackknife Relationship |
| 10 | Oct 26: EM Algorithm | Oct 27 | Oct 28: EM Algorithm | Oct 29 | Oct 30: EM Algorithm |
| 11 | Nov 2: Exam 2 | Nov 3 | Nov 4: EM Algorithm Convergence | Nov 5 | Nov 6: Homework 5 due. EM Algorithm Convergence |
| 12 | Nov 9: Monte Carlo | Nov 10: | Nov 11: No class | Nov 12 | Nov 13: Rejection Sampling |
| 13 | Nov 16: Importance Sampling | Nov 17 | Nov 18: Markov Chains | Nov 19 | Nov 20: Homework 6 due, Markov Chains |
| 14 | Nov 23: Metropolis-Hastings | Nov 24 | Nov 25: No class | Nov 26 | Nov 27: No class |
| 15 | Nov 30: Markov Chain Monte Carlo | Dec 1 | Dec 2: Review of Course | Dec 3 | Dec 4: Homework 7 due, Review of Course |
Advice on succeeding in a math class:
Homework
Homework .tex files
Supplementary Notes