Time: Monday, Wednesday, Friday at 10:00 AM - 10:50 AM
Location: Taft Hall 207
Instructor: Jie Yang
Office: SEO 513
Phone: (312) 413-3748
E-Mail: jyang06 AT uic DOT edu
Office Hours: Monday, Wednesday, Friday at 2:00 p.m. - 3:00 p.m. (or by appointment)
Textbook: Geof H. Givens and
Jennifer A. Hoeting,
Computational Statistics,
John Wiley & Sons, Inc., 2nd edition, 2013.
Preview table of contents and preface.
Course Contents: EM Optimization Methods, Simulation and Monte Carlo Integration, Markov Chain Monte Carlo, Bootstrapping, Nonparametric Density Estimation, Bivariate Smoothing
Prerequisite: STAT 411 or consent of instructor.
Homework:
Turn in every Friday before class;
half of the grade counts for completeness;
half of the grade counts for correctness of one selected problem.
Project: Students are required to work in groups on course projects and submit their final reports before May 1st, Friday, 10:00 am.
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 40%, Project 60%
Grading Scale: 90% A , 80% B , 70% C , 60% D
| WEEK | SECTIONS | BRIEF DESCRIPTION |
| 01/13 - 01/17 | Introduction; 6.1; 6.2 | Introduction to the Monte Carlo method; Exact simulation |
| 01/20 - 01/24 | Holiday; 6.2; 6.3 | Exact simulation; Approximate simulation |
| 01/27 - 01/31 | 6.3; 6.4; 6.4 | Approximate simulation; Variance reduction techniques |
| 02/03 - 02/07 | 1.7; 7.1; 7.1 | Markov chains; Metropolis-Hastings algorithm |
| 02/10 - 02/14 | 7.2; 7.2; 7.3 | Gibbs sampling; Implementation |
| 02/17 - 02/21 | 9.1; 9.2; 9.2 | The bootstrap principle; Basic methods |
| 02/24 - 02/28 | 9.2; 9.3; 9.3 | Basic methods; Bootstrap inference |
| 03/02 - 03/06 | 9.8; 4.1; 4.1 | Permutation tests; Missing data, marginalization, and notation |
| 03/09 - 03/13 | 4.2; 4.2; 4.2 | The EM algorithm |
| 03/30 - 04/03 | 4.3; 4.3; 10.1 | EM Variants; Measures of performance |
| 04/06 - 04/10 | 10.2; 10.2; 10.3 | Kernel density estimation; Nonkernel methods |
| 04/13 - 04/17 | 11.1; 11.2; 11.2 | Predictor-response data; Linear smoothers |
| 04/20 - 04/24 | 11.3; 11.3; 11.4 | Comparison of linear smoothers; Nonlinear smoothers |
| 04/27 - 05/01 | 11.4; 11.5; 11.5 | Nonlinear smoothers; Confidence bands |