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<p>Quantile regression (QR) provides a flexible statistical framework for modeling the entire conditional distribution of the response variable, making it useful for analysis in various fields. Despite its advantages, existing methods for QR often encounter numerical challenges in high-dimensional settings, especially for those with ordinal responses. In this paper, we use a latent-response framework to construct a Bayesian hierarchical model to conduct parameter estimation and variable selection for ordinal QR. Using the asymmetric Laplace working likelihood and the horseshoe prior for the regression coefficients, we obtain the posterior samples to be screened by the sequential two-means clustering process to identify significant predictors. Extensive numerical results via simulation studies and two real-data applications demonstrate the competitive performance of our approach over some existing Bayesian ordinal data analysis methods. The illustrative datasets on youth educational attainment and liver cancer methylation highlight the practical utility of our proposed approach in both low- and high-dimensional scenarios.</p>

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Bayesian variable selection in high-dimensional ordinal quantile regression models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Mai Dao,
  • Md Sakhawat Hossain,
  • Zhuanzhuan Ma

摘要

Quantile regression (QR) provides a flexible statistical framework for modeling the entire conditional distribution of the response variable, making it useful for analysis in various fields. Despite its advantages, existing methods for QR often encounter numerical challenges in high-dimensional settings, especially for those with ordinal responses. In this paper, we use a latent-response framework to construct a Bayesian hierarchical model to conduct parameter estimation and variable selection for ordinal QR. Using the asymmetric Laplace working likelihood and the horseshoe prior for the regression coefficients, we obtain the posterior samples to be screened by the sequential two-means clustering process to identify significant predictors. Extensive numerical results via simulation studies and two real-data applications demonstrate the competitive performance of our approach over some existing Bayesian ordinal data analysis methods. The illustrative datasets on youth educational attainment and liver cancer methylation highlight the practical utility of our proposed approach in both low- and high-dimensional scenarios.