JSM 2005 - Toronto

Abstract #304748

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.



The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page
Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 271
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304748
Title: Estimation and Model Choice in Nonparametric Additive Regression
Author(s): Ivan Jeliazkov*+ and Siddhartha Chib
Companies: University of California, Irvine and Washington University in St. Louis
Address: 3151 Social Science Plaza, Irvine, CA, 92697-5100, United States
Keywords: Additive models ; Bayes factor ; Marginal likelihood ; Markov chain Monte Carlo ; Gibbs sampling ; model comparison
Abstract:

This article revisits the Bayesian inferential problem for the important class of nonparametric additive models. We propose a new identification restriction on the unknown covariate functions under which the model can be estimated efficiently by Markov chain Monte Carlo simulation techniques. In contrast to previous discussions in the literature, our estimation procedure is based on proper smoothness priors on the unknown functions. This opens the way for model comparisons on the basis of marginal likelihoods and Bayes factors. A simulation study is used to illustrate the performance of the proposed techniques. The entire methodology is conveniently adapted to generalized additive models for both nonclustered and clustered data.

  • The address information is for the authors that have a + after their name.
  • Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program

JSM 2005 For information, contact jsm@amstat.org or phone (888) 231-3473. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2005