Bayesian Adaptive Lasso for Ordinal Regression With Latent Variables

Xiang-Nan Feng, Hao-Tian Wu and Xin-Yuan Song

Sociological Methods & Research, 2017, vol. 46, issue 4, 926-953

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.

Keywords: Bayesian adaptive lasso; latent variable; MCMC methods; ordinal response (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:46:y:2017:i:4:p:926-953

DOI: 10.1177/0049124115610349

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