Bayesian density regression
Bayesian density regression
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DOI：
10.1111/j.1467-9868.2007.00582.x
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发表时间：
2007-01-01
影响因子：
5.8
通讯作者：
Park, Ju-Hyun
中科院分区：
文献类型：
--
作者：
Dunson, David B.;Pillai, Natesh;Park, Ju-Hyun
本文考虑贝叶斯方法的密度回归，允许一个随机的概率分布变化灵活的多个预测。条件响应分布表示为非参数混合回归模型，混合分布随预测变量而变化。针对混合分布的不可数集合，提出了一类Dirichlet过程先验的加权混合。它示出，该规格的结果在一个广义的波利亚瓮计划，它采用的权重是依赖于受试者的预测值之间的距离。为了允许局部依赖的混合分布，我们提出了一个基于核的加权方案。提出了一种用于后验计算的Gibbs抽样算法。通过使用模拟数据的例子和流行病学的应用程序的方法进行说明。
The paper considers Bayesian methods for density regression, allowing a random probability distribution to change flexibly with multiple predictors. The conditional response distribution is expressed as a non-parametric mixture of regression models, with the mixture distribution changing with predictors. A class of weighted mixture of Dirichlet process priors is proposed for the uncountable collection of mixture distributions. It is shown that this specification results in a generalized Polya urn scheme, which incorporates weights that are dependent on the distance between subjects' predictor values. To allow local dependence in the mixture distributions, we propose a kernel-based weighting scheme. A Gibbs sampling algorithm is developed for posterior computation. The methods are illustrated by using simulated data examples and an epidemiologic application.