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<p>This study presents Bayesian analysis of the quantile regression model for right-censored data from the exponentiated exponential distribution. In the case of quantile regression, Bayesian analysis always leads to analytically intractable posteriors. To address this challenge, a gradient-based Metropolis-Hastings algorithm is used on Barker proposal. We perform Bayesian model selection for various combinations of covariates using the Bayesian Information Criterion, the Deviance Information Criterion, and the logarithm pseudo-marginal likelihood. Numerical illustrations are provided for simulated and real datasets. For a real study, endometrial cancer survival data is considered.</p>

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Bayesian quantile regression for right censored survival data from exponentiated exponential distribution

  • 期刊论文

  • 发表日期：

    2026年7月4日

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  • Tripti Tripathi,
  • Rakesh Ranjan,
  • Vikas Kumar Sharma

摘要

This study presents Bayesian analysis of the quantile regression model for right-censored data from the exponentiated exponential distribution. In the case of quantile regression, Bayesian analysis always leads to analytically intractable posteriors. To address this challenge, a gradient-based Metropolis-Hastings algorithm is used on Barker proposal. We perform Bayesian model selection for various combinations of covariates using the Bayesian Information Criterion, the Deviance Information Criterion, and the logarithm pseudo-marginal likelihood. Numerical illustrations are provided for simulated and real datasets. For a real study, endometrial cancer survival data is considered.