Abstract Details
Activity Number:
|
130
|
Type:
|
Contributed
|
Date/Time:
|
Monday, August 4, 2014 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract #311389
|
View Presentation
|
Title:
|
Bayesian Variable Selection for Median Latent Variable Model
|
Author(s):
|
Yifan Wang*+ and Zhibin Xu and Hong Ji
|
Companies:
|
and Capital University of Economics and Business and Capital University of Economics and Business
|
Keywords:
|
median regression ;
confirmatory factor analysis model ;
normal-gamma prior ;
asymmetric Laplace distribution ;
Markov chain Monte Carlo
|
Abstract:
|
In biomedical, psychological, social, and behavioral sciences, it is very common to encounter latent variables along with non-normal data. We propose a median latent variable model to deal with this kind of data in a Bayesian framework. The normal-gamma prior distribution is applied here for simultaneous estimation and model selection. A Markov chain Monte Carlo (MCMC) algorithm for obtaining Bayesian estimates is developed. Simulation studies are carried out to examine the finite sample performance of the proposed estimators. We illustrate the proposed method with a real data set from a longitudinal study of polydrug use.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.