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New aggregation method ensures finite-sample FDR control in distributed network testing

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces Cross-Fit Greedy Aggregation (CFGA), an advancement in distributed multiple testing over networks. This method addresses limitations in previous greedy aggregation algorithms by eliminating a winner's-curse bias, thereby ensuring finite-sample False Discovery Rate (FDR) control. The paper also proposes variants like BONuS-GA and e-CFGA, which further refine FDR control and communication efficiency, demonstrating empirical dominance in various sample size scenarios. AI

RANK_REASON The cluster contains a single academic paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New aggregation method ensures finite-sample FDR control in distributed network testing

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The cluster contains a single academic paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Mehrdad Pournaderi ·

    On Large-Scale Multiple Testing Over Networks: A Non-Asymptotic Approach

    arXiv:2609.14170v1 Announce Type: cross Abstract: Distributed multiple testing asks $N$ sites to control a global false discovery rate (FDR) under a tight communication budget. The greedy interval-aggregation algorithm of Pournaderi and Xiang (2024) solves this asymptotically but…

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