协变量局部化错误发现率

中文摘要 AI 辅助

我们引入了一个灵活的协变量依赖多重检验模型，该模型可使用非参数高斯混合模型进行编码。权重局部化预测递归（PRx）是牛顿预测递归算法方法论中的一项新发展，随后被用于估计该混合模型的组成部分，从而能够通过单一统一算法恢复协变量局部化错误发现率 $\ ext{Pr}(H_i = 0|z_i,x_i)$。该量代表了Efron局部错误发现率向协变量依赖设置的最直接扩展，并在简单拒绝规则下具有可证明的贝叶斯FDR控制性质。我们提出了几种用于估计和阈值化局部错误发现率的程序，并通过各种模拟和真实数据示例表明，我们的程序能提高检验功效、实现更严格的贝叶斯FDR控制，并产生更可解释的拒绝结果。此外，我们证明这在固定和随机假设标签下均成立，表明我们提出的方法在多重检验的频率派和贝叶斯派解释下均表现良好。

英文摘要

We introduce a flexible model for covariate-dependent multiple testing which can be encoded using a nonparametric Gaussian mixture model. Weight-localized predictive recursion (PRx), a new development in the methodology of Newton's predictive recursion algorithm, is then leveraged to estimate the components of this mixture model, allowing for recovery of the covariate-localized false discovery rate $\text{Pr}(H_i = 0|z_i,x_i)$ using a single, unified algorithm. This quantity represents the most direct extension of Efron's local false discovery rate to the covariate-dependent setting, and admits provable Bayesian FDR control properties under simple rejection rules. We introduce several procedures for estimating and thresholding the local false discovery rate, and show using various simulations and a real-data example that our procedures lead to increased power, tighter Bayesian FDR control, and more interpretable rejections. We furthermore show that this holds for fixed and randomized hypothesis labels, indicating that our proposed methods perform well under both frequentist and Bayesian interpretations of multiple testing.

发表机构

• Duke University（杜克大学）

机构由 AI 辅助整理，请以论文原文为准。