Submitted by Tena Katsaounis, SPES Education Chair
SPES is sponsoring the following short course at the Joint Statistical Meetings 2010. Please check the upcoming JSM2010 online program for courses dates and sites.
See you at JSM2010!
Tena Katsaounis
SPES Continuing Education Chair
Maria Rizzo and Jim Albert are professors in the Department of Mathematics and Statistics at Bowling Green State University. Dr. Rizzo regularly teaches a doctoral-level course in statistical computing and has recently published a text on statistical computing using R. Dr. Albert has regularly taught a course in Bayesian inference and has written several texts on Bayesian modeling and computation. Dr. Albert has previously taught short courses at JSM on ordinal data modeling (with Val Johnson) and on the use of sports in teaching statistics.
This course describes the use of the statistical system R in Monte Carlo experiments, simulation-based inference, and Bayesian computation. R tools are described for generating random variables, computing criteria of statistical procedures, and replicating the procedure to compute quantities such as mean squared error and probability of coverage. R commands for implementing simulation-based procedures such as bootstrap and permutation tests are outlined. The use of R in Bayesian computation is described, including the programming of the posterior distribution and the use of different R tools to summarize the posterior. Special focus will be on the application of Markov chain Monte Carlo algorithms and diagnostic methods to assess convergence of the algorithms.
The participants will learn about how R can be used to simulate random variates and design a Monte Carlo experiment to learn about a property of a statistical procedure. The participant will learn how to implement simulation-based inferential procedures on R such as the bootstrap and permutation test. In addition, the participant will learn how to write a R function to define a posterior density in Bayesian inference, and how to use different R tools to simulate from the posterior distribution and summarize the simulated sample to perform inferences.
This course describes the use of the statistical system R in Monte Carlo experiments, simulation-based inference, and Bayesian computation. R tools are described for generating random variables, computing criteria of statistical procedures, and replicating the procedure to compute quantities such as mean squared error and probability of coverage. R commands for implementing simulation-based procedures such as bootstrap and permutation tests are outlined. The use of R in Bayesian computation is described, including the programming of the posterior distribution and the use of different R tools to summarize the posterior. Special focus will be on the application of Markov chain Monte Carlo algorithms and diagnostic methods to assess convergence of the algorithms.