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Activity Number: 535
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #315175
Title: Bayesian Adaptive Lasso for Ordinal Regression with Latent Variables
Author(s): Xiang-Nan Feng* and Xinyuan Song and Hao-Tian Wu
Companies: The Chinese University of Hong Kong and and Sun Yat-Sen University
Keywords: Bayesian adaptive lasso ; Ordinal response ; Latent variables ; MCMC methods
Abstract:

We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct simultaneous estimation and variable selection. Nice features including empirical performance of the proposed methodology are demonstrated by simulation studies. The model is applied to a study on happiness and its potential determinants from the Inter-university Consortium for Political and Social Research.


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