Time: Monday, Wednesday, Friday at 10:00 AM - 10:50 AM
Location: Taft Hall 207
Textbook: Geof H. Givens and
Jennifer A. Hoeting,
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.
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
|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|