Nonparametric Bayes Conditional Distribution Modeling With Variable Selection.

Nonparametric Bayes Conditional Distribution Modeling With Variable Selection.
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DOI:
10.1198/jasa.2009.tm08302
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发表时间:
2009-12-01
影响因子:
3.7
通讯作者:
Dunson DB
Dunson DB
中科院分区:
数学1区
文献类型:
--
作者:
Chung Y;Dunson DB

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本文考虑了一种灵活表征响应和多个预测因子之间关系的方法。目标是(1)估计处理整个预测空间分布变化的条件响应分布,以及(2)确定局部区域和全球范围内响应分布变化的重要预测因子。我们首先引入概率断裂过程(PSBP)作为不可数预测依赖随机分布集合的先验,并提出一种正态回归的PSBP混合(PSBPM)用于对条件分布建模。采用全局变量选择结构来丢弃不重要的预测因子,同时允许估计后验包含概率。根据不同预测点的条件分布估计进行局部变量选择。提出了一种有效的随机搜索采样后验计算算法。这些方法通过模拟加以说明,并应用于流行病学研究。
This article considers a methodology for flexibly characterizing the relationship between a response and multiple predictors. Goals are (1) to estimate the conditional response distribution addressing the distributional changes across the predictor space, and (2) to identify important predictors for the response distribution change both within local regions and globally. We first introduce the probit stick-breaking process (PSBP) as a prior for an uncountable collection of predictor-dependent random distributions and propose a PSBP mixture (PSBPM) of normal regressions for modeling the conditional distributions. A global variable selection structure is incorporated to discard unimportant predictors, while allowing estimation of posterior inclusion probabilities. Local variable selection is conducted relying on the conditional distribution estimates at different predictor points. An efficient stochastic search sampling algorithm is proposed for posterior computation. The methods are illustrated through simulation and applied to an epidemiologic study.
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