Bayesian nonparametric inference on stochastic ordering
Bayesian nonparametric inference on stochastic ordering
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DOI：
10.1093/biomet/asn043
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发表时间：
2008-12-01
期刊：
影响因子：
2.7
通讯作者：
Peddada, Shyamal D.
中科院分区：
文献类型：
--
作者：
Dunson, David B.;Peddada, Shyamal D.
我们考虑贝叶斯推理集合的未知分布服从部分随机序。为了解决群体之间的平等性测试和群体特定分布的估计问题，我们提出了限制依赖Dirichlet过程先验类。这些先验在随机有序分布空间中具有完全的支持，并且可以用于未知混合分布的集合，以获得灵活的一类混合模型。理论属性进行了讨论，有效的方法开发后验计算使用马尔可夫链蒙特卡罗模拟和方法说明使用的数据从DNA损伤和修复的研究。
We consider Bayesian inference about collections of unknown distributions subject to a partial stochastic ordering. To address problems in testing of equalities between groups and estimation of group-specific distributions, we propose classes of restricted dependent Dirichlet process priors. These priors have full support in the space of stochastically ordered distributions, and can be used for collections of unknown mixture distributions to obtain a flexible class of mixture models. Theoretical properties are discussed, efficient methods are developed for posterior computation using Markov chain Monte Carlo simulation and the methods are illustrated using data from a study of DNA damage and repair.