Time: Monday, Wednesday, Friday at 9:00 - 9:50 a.m.
Location (in person and on campus): Lecture Center Building A (805 S Morgan St, Chicago, IL 60607), Room A005
Instructor: Jie Yang
Office: SEO 513
Phone: (312) 413-3748
E-Mail: jyang06 AT uic DOT edu
Office Hours: Monday, Wednesday, Friday at 11:00 a.m. - 11:50 a.m. at UIC Zoom or by appointment
Textbook (required): R. V. Hogg, J. W. McKean, A. T. Craig,
Introduction to Mathematical Statistics, 8th edition, 2019.
Reference Books (optional):
Attendance/Participation Policy:
Students are expected to attend the lectures and participate in the discussions. Attendance will be counted at least six times during the course period and the students who present may receive half credit point each time and up to three extra credit points in total on the final grade at a 100-point scale. Students who actively participate in the discussion may receive half or one credit point each time and up to five credit points in total. If for any reason you could not present in class on time, please send me an email at your earliest convenience. If you need special accommodations due to disabilities, please contact the Disability Resource Center for a Letter of Accommodation (LOA).
Assignments, Due Dates, and Deadlines:
Homework will be assigned at a weekly basis;
turn in your homework every Wednesday before class via UIC Canvas;
half of the grade counts for completeness;
half of the grade counts for correctness of one selected problem.
Policy for Missed or Late Homework: Students may request up to two days' extension for each homework; late homework without request ahead of time or longer than two days will not be accepted; the lowest two homework scores will be dropped for final grade.
Exams: This course will require students to be on campus for in-person exams on October 2nd (Friday) and November 13th (Friday), 9:00 AM - 9:50 AM.
Project: Students are required to work by themselves or in groups on course projects and submit their final reports before December 4th, 2026, Friday, 9:00am.
Each group should consist of at most three students. The projects may come from the optional problems assigned
by the instructor or be proposed by the students themselves upon the approval of the instructor.
Grading: Homework 20%, Two Exams 25% each, Project 30%.
Grading Scale: 90% A , 80% B , 70% C , 60% D.
Format of All Exams: Each exam is based on the homework and the examples discussed in class. The last class session before each exam is a review session. Please prepare any questions that you may have.
No makeup exam will be given without a valid excuse.
| WEEK | SECTIONS | BRIEF DESCRIPTION |
| 08/24 - 08/28 | 3.1, 3.2, 3.3; 3.4, 3.5, 3.6; 4.1, 4.2, 4.4 | Review on Special Distributions; Sampling and Statistics, Confidence Interval, Order Statistics |
| 08/31 - 09/04 | 5.3; 6.1; 6.1 | Central Limit Theorem; Maximum Likelihood Estimation |
| 09/07 - 09/11 | Holiday; 6.2; 6.2 | Rao-Cramer Lower Bound and Efficiency |
| 09/14 - 09/18 | 6.3; 6.3; 6.4 | Maximum Likelihood Tests; Multiparameter Case: Estimation |
| 09/21 - 09/25 | 6.4; 7.1; 7.1 | Multiparameter Case: Estimation; Measures of Quality of Estimators |
| 09/28 - 10/02 | 7.1; Review; Exam-I | Measures of Quality of Estimators |
| 10/05 - 10/09 | 7.2; 7.2; 7.3 | A Sufficient Statistic for a Parameter; Properties of a Sufficient Statistic |
| 10/12 - 10/16 | 7.4; 7.5; 7.5 | Completeness and Uniqueness; The Exponential Class of Distributions |
| 10/19 - 10/23 | 7.7; 7.7; 7.8 | The Case of Several Parameters; Minimal Sufficiency and Ancillary Statistics |
| 10/26 - 10/30 | 7.8; 7.9; 7.9 | Minimal Sufficiency and Ancillary Statistics; Sufficiency, Completeness and Independence |
| 11/02 - 11/06 | 8.1; 8.1; 8.2 | Most Powerful Tests; Uniformly Most Powerful Tests |
| 11/09 - 11/13 | 8.2; Review; Exam-II | Uniformly Most Powerful Tests |
| 11/16 - 11/20 | 8.2; 8.3; 8.3 | Uniformly Most Powerful Tests; Likelihood Ratio Tests |
| 11/23 - 11/27 | 11.1; Holiday; Holiday | Likelihood Ratio Tests; Bayesian Procedures |
| 11/30 - 12/04 | 11.1; 11.3; 11.4 | Bayesian Procedures; Gibbs Sampler; Modern Bayesian Methods |