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Bayesian adaptive lasso quantile regression with non-ignorable missing responses

Author

Listed:
  • Ranran Chen

    (The University of Texas at San Antonio)

  • Mai Dao

    (Wichita State University)

  • Keying Ye

    (The University of Texas at San Antonio)

  • Min Wang

    (The University of Texas at San Antonio)

Abstract

In this paper, we develop a fully Bayesian adaptive lasso quantile regression model to analyze data with non-ignorable missing responses, which frequently occur in various fields of study. Specifically, we employ a logistic regression model to deal with missing data of non-ignorable mechanism. By using the asymmetric Laplace working likelihood for the data and specifying Laplace priors for the regression coefficients, our proposed method extends the Bayesian lasso framework by imposing specific penalization parameters on each regression coefficient, enhancing our estimation and variable selection capability. Furthermore, we embrace the normal-exponential mixture representation of the asymmetric Laplace distribution and the Student-t approximation of the logistic regression model to develop a simple and efficient Gibbs sampling algorithm for generating posterior samples and making statistical inferences. The finite-sample performance of the proposed algorithm is investigated through various simulation studies and a real-data example.

Suggested Citation

  • Ranran Chen & Mai Dao & Keying Ye & Min Wang, 2025. "Bayesian adaptive lasso quantile regression with non-ignorable missing responses," Computational Statistics, Springer, vol. 40(3), pages 1643-1682, March.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:3:d:10.1007_s00180-024-01546-6
    DOI: 10.1007/s00180-024-01546-6
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    References listed on IDEAS

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