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Controlling the false discovery rate in high-dimensional linear models using model-X knockoffs and $p$ -valuesControlling the false discovery rate in high-dimensional linear models using model-X knockoffs and $p$-values

Jinyuan Chang · Chenlong Li · Cheng Yong Tang · Zhengtian Zhu首次提交 2025-05-22 · 修订 2026-10-14 · v3arXiv预印本

结合 model-X knockoffs 与 p 值的高维线性模型 FDR 控制AI 解读

We propose a novel multiple testing methodology for controlling the false discovery rate (FDR) in high-dimensional linear models that integrates model-X knockoff techniques with debiased penalized regression estimators. At the foundation of our methodology, we construct and study two sets of naturally paired high-dimensional test statistics and the associated $p$ -values for evaluating the same null hypotheses. The first set is shown to be asymptotically mutually independent,…We propose a novel multiple testing methodology for controlling the false discovery rate (FDR) in high-dimensional linear models that integrates model-X knockoff techniques with debiased penalized regression estimators. At the foundation of our methodology, we construct and study two sets of naturally paired high-dimensional test statistics and the associated $p$-values for evaluating the same null hypotheses. The first set is shown to be asymptotically mutually independent,…

多重检验与同时推断正则化与变量选择

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-14

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Controlling the false discovery rate in high-dimensional linear models using model-X knockoffs and ppp-valuesControlling the false discovery rate in high-dimensional linear models using model-X knockoffs and $p$-values

Controlling the false discovery rate in high-dimensional linear models using model-X knockoffs and $p$ -valuesControlling the false discovery rate in high-dimensional linear models using model-X knockoffs and $p$-values