JSM 2005 - Toronto

Abstract #303034

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: 56
Type: Topic Contributed
Date/Time: Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303034
Title: Variable Selection in Semiparametric Mixed Effects Models
Author(s): Bo Cai*+ and
Companies: National Institute of Environmental Health Sciences and National Institute of Environmental Health Sciences
Address: POBox 12233 MD A3 03, Durham, NC, 27709, United States
Keywords: Dirichlet process ; Latent variables ; Nonparametric Bayes ; Reparameterization ; Stochastic search ; Variable selection
Abstract:

In analyzing longitudinal or clustered data with a mixed effects model (Laird and Ware 1982), one may be concerned about violations of normality of the random effects and residuals. Such violations can potentially impact subset selection for the fixed and random effect components of the model and inferences on the heterogeneity structure. To address this problem, this article proposes a Bayesian approach for variable selection in semiparametric mixed effects models. To avoid parametric assumptions on the densities of the residuals and the random effects for predictors included in the model, we use Dirichlet process mixtures of normals. We also allow uncertainty in the predictors to be included in the fixed and random effect components through variable selection priors. A stochastic search Gibbs sampler is developed and the methods are illustrated using simulated and real data from an epidemiologic study.

  • 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