1. Bootstrap: introduction to bootstrap, empirical distribution function and plug-in method, bootstrap for estimation of standard errors, bootstrap for estimation of bias, jackknife, bootstrop for construction of confidence intervals.
2. EM-algorithm: general theroy arising from missing data settings, application to hidden Markov models with brackground of DNA sequence alignment analysis.
3. Monte Carlo: numerical approximation to integrals, MCMC, application to parameter estimation in linear mixed models.
See more details at Course Outline. Basic information regarding course evaluations and tentative topics to be covered by this course.
See Assignment 1.
See Assignment 2.
See Project 1 and data.
See Assignment 5 data.