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Bayesian Adaptive Lasso for Ordinal Regression With Latent Variables

Author

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  • Xiang-Nan Feng
  • Hao-Tian Wu
  • Xin-Yuan Song

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.

Suggested Citation

  • Xiang-Nan Feng & Hao-Tian Wu & Xin-Yuan Song, 2017. "Bayesian Adaptive Lasso for Ordinal Regression With Latent Variables," Sociological Methods & Research, , vol. 46(4), pages 926-953, November.
    • Handle: RePEc:sae:somere:v:46:y:2017:i:4:p:926-953
    DOI: 10.1177/0049124115610349
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    References listed on IDEAS

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    Cited by:

    1. Feng, Xiangnan & Lu, Bin & Song, Xinyuan & Ma, Shuang, 2019. "Financial literacy and household finances: A Bayesian two-part latent variable modeling approach," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 119-137.

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