Homework #3, data for Problem 6.4, due.Predictor-response data Linear smoothersĬomparison of linear smoothers Nonlinear smoothers Kernel density estimation Nonkernel methods Permutation tests Missing data, marginalization, and notation Markov chains Metropolis-Hastings algorithm Introduction to the Monte Carlo method Exact simulationĪpproximate simulation Variance reduction techniques 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. Project: Students are required to work in groups on course projects and submit their final reports before May 1st, Friday, 10:00 am. Half of the grade counts for correctness of one selected problem. Half of the grade counts for completeness Prerequisite: STAT 411 or consent of instructor. John Wiley & Sons, Inc., 2nd edition, 2013.Ĭourse Contents: EM Optimization Methods, Simulation and Monte Carlo Integration, Markov Chain Monte Carlo, Bootstrapping, Nonparametric Density Estimation, Bivariate Smoothing Office Hours: Monday, Wednesday, Friday at 2:00 p.m. Time: Monday, Wednesday, Friday at 10:00 AM - 10:50 AM Course information for Stat 451 STAT 451 (37112, 37113) Computational Statistics Spring Semester 2020
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