Covariate-localized False Discovery Rates

2026-09-08

AI Summary

研究提出一种基于非参数高斯混合模型和权重局部预测递归算法的方法，以估计协变量依赖下的局部错误发现率，提高多重假设检验的效能和解释性。

Abstract

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.

Problem

• Covariate-dependent multiple testing
• False discovery rate
• Predictive recursion

Innovation

• covariate-localized false discovery rate
• nonparametric Gaussian mixture model
• weight-localized predictive recursion (PRx)
• Bayesian FDR control