Abstract

We propose two Bayesian methods for regularized left censored regression: the reciprocal Bayesian bridge and the reciprocal Bayesian adaptive bridge. Gibbs samplers are derived based on the reciprocal Bayesian bridge prior which can be written as a scale mixture of inverse uniform distribution. The proposed approaches are then illustrated via five simulated studies and a real data example. Compared with some existing methods, our methods have improved variable selection and estimation performance in both simulations and the real data example.

中文翻译：

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左删失数据的倒数贝叶斯桥

摘要

我们提出了两种用于正则化左删失回归的贝叶斯方法：倒数贝叶斯桥和倒数贝叶斯自适应桥。吉布斯采样器是基于倒数贝叶斯桥先验推导出来的，可以将其写为逆均匀分布的尺度混合。然后通过五项模拟研究和一个真实数据示例来说明所提出的方法。与一些现有方法相比，我们的方法在模拟和实际数据示例中都提高了变量选择和估计性能。