论文标题
在默认的先验方面，可用于强大的贝叶斯估计与分歧
On default priors for robust Bayesian estimation with divergences
论文作者
论文摘要
本文为基于差异的异常值提供了客观的贝叶斯估计，介绍了客观的先验。最低$γ$ - 差估计量众所周知，可以很好地估计对重污染。近年来，还提出了通过使用基于差异的准阶层分布的稳健贝叶斯方法。在客观的贝叶斯框架中，在此类准阶层分布下选择默认的先验分布是一个重要问题。在这项研究中，我们根据基于$γ$ divergence提供了一些参考和力矩匹配先验的特性。特别是，我们表明，在污染分布的条件下，所提出的先验在污染比的任何条件下都大致健壮。还提供了一些模拟研究。
This paper presents objective priors for robust Bayesian estimation against outliers based on divergences. The minimum $γ$-divergence estimator is well-known to work well estimation against heavy contamination. The robust Bayesian methods by using quasi-posterior distributions based on divergences have been also proposed in recent years. In objective Bayesian framework, the selection of default prior distributions under such quasi-posterior distributions is an important problem. In this study, we provide some properties of reference and moment matching priors under the quasi-posterior distribution based on the $γ$-divergence. In particular, we show that the proposed priors are approximately robust under the condition on the contamination distribution without assuming any conditions on the contamination ratio. Some simulation studies are also presented.
