Response-guided knockoffs for directional FDR control in linear models

2026-08-29

AI Summary

本文针对线性模型中的特征选择问题，提出了一种响应引导的敲除滤波器方法，通过利用噪声扰动响应来指导敲除构造，并在有限样本下控制方向错误发现率。

Abstract

We consider the problem of feature selection in linear models with finite-sample control of the false discovery rate (FDR). While existing knockoff-based methods control the directional FDR, which penalises incorrect sign estimates, they do not target discoveries in a pre-specified direction, and their knockoff constructions are entirely response-agnostic. We introduce the response-guided knockoff filter, which leverages a noise-perturbed version of the response to guide knockoff construction toward features likely to have the target sign, while provably controlling the directional FDR. The method operates under a weaker sample-size requirement $n > p + 2$, compared to $n \geq 2p$ required by existing fixed-X generators. Simulations and HIV drug resistance experiments demonstrate power gains over existing methods.

Problem

• feature selection
• false discovery rate
• directional FDR

Innovation

• response-guided knockoff filter
• directional FDR control
• noise-perturbed response
• power gains