An adaptive procedure for detecting replicated signals with $k$-family-wise error rate control
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911113890758656 |
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| author | Tran, Ninh |
| author_facet | Tran, Ninh |
| contents | Partial conjunction (PC) hypothesis testing is widely used to assess the replicability of scientific findings across multiple comparable studies. In high-throughput meta-analyses, testing a large number of PC hypotheses with k-family-wise error rate (k-FWER) control often suffers from low statistical power due to the multiplicity burden. The state-of-the-art AdaFilter-Bon procedure by Wang et al. (2022, Ann. Stat., 50(4), 1890-1909) alleviates this problem by filtering out hypotheses unlikely to be false before applying a rejection rule. However, a side effect of filtering is that it renders the rejection rule more stringent than necessary, leading to conservative k-FWER control. In this paper, we mitigate this conservativeness - and thereby improve the power of AdaFilter-Bon - by incorporating a post-filter null proportion estimate into the procedure. The resulting method, AdaFilter-AdaBon, has proven asymptotic k-FWER control under weak dependence and demonstrates empirical finite-sample control with higher power than the original AdaFilter-Bon in simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An adaptive procedure for detecting replicated signals with $k$-family-wise error rate control Tran, Ninh Methodology 62F03 Partial conjunction (PC) hypothesis testing is widely used to assess the replicability of scientific findings across multiple comparable studies. In high-throughput meta-analyses, testing a large number of PC hypotheses with k-family-wise error rate (k-FWER) control often suffers from low statistical power due to the multiplicity burden. The state-of-the-art AdaFilter-Bon procedure by Wang et al. (2022, Ann. Stat., 50(4), 1890-1909) alleviates this problem by filtering out hypotheses unlikely to be false before applying a rejection rule. However, a side effect of filtering is that it renders the rejection rule more stringent than necessary, leading to conservative k-FWER control. In this paper, we mitigate this conservativeness - and thereby improve the power of AdaFilter-Bon - by incorporating a post-filter null proportion estimate into the procedure. The resulting method, AdaFilter-AdaBon, has proven asymptotic k-FWER control under weak dependence and demonstrates empirical finite-sample control with higher power than the original AdaFilter-Bon in simulations. |
| title | An adaptive procedure for detecting replicated signals with $k$-family-wise error rate control |
| topic | Methodology 62F03 |
| url | https://arxiv.org/abs/2508.15363 |