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arXiv Statistics ML By Mengtao Wen, Haojie Ren, Changliang Zou

Controlling the False Discovery Rate via Model-Agnostic Importance Measures

Read the original on arXiv Statistics ML →

The paper introduces MAS (Model‑Agnostic Selection with agent‑X importance), a new method for controlling the false discovery rate (FDR) in variable selection that does not rely on specific model assumptions. MAS uses sample splitting, a novel agent‑X importance measure, a resplit‑per‑covariate strategy, and data aggregation to achieve asymptotic FDR control while improving computational efficiency and power. The authors provide theoretical guarantees and demonstrate competitive performance through extensive simulations and real‑data analyses.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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