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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2210.17121 |
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| _version_ | 1866917744964796416 |
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| author | Deng, Linsui He, Kejun Zhang, Xianyang |
| author_facet | Deng, Linsui He, Kejun Zhang, Xianyang |
| contents | Clustered effects are often encountered in multiple hypothesis testing of spatial signals. In this paper, we propose a new method, termed \textit{two-dimensional spatial multiple testing} (2d-SMT) procedure, to control the false discovery rate (FDR) and improve the detection power by exploiting the spatial information encoded in neighboring observations. The proposed method provides a novel perspective of utilizing spatial information by gathering signal patterns and spatial dependence into an auxiliary statistic. 2d-SMT rejects the null when a primary statistic at the location of interest and the auxiliary statistic constructed based on nearby observations are greater than their corresponding cutoffs. 2d-SMT can also be combined with different variants of the weighted BH procedures to improve the detection power further. A fast algorithm is developed to accelerate the search for optimal cutoffs in 2d-SMT. In theory, we establish the asymptotic FDR control of 2d-SMT under weak spatial dependence. Extensive numerical experiments demonstrate that the 2d-SMT method combined with various weighted BH procedures achieves the most competitive performance in FDR and power trade-off. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_17121 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Powerful Spatial Multiple Testing via Borrowing Neighboring Information Deng, Linsui He, Kejun Zhang, Xianyang Methodology Clustered effects are often encountered in multiple hypothesis testing of spatial signals. In this paper, we propose a new method, termed \textit{two-dimensional spatial multiple testing} (2d-SMT) procedure, to control the false discovery rate (FDR) and improve the detection power by exploiting the spatial information encoded in neighboring observations. The proposed method provides a novel perspective of utilizing spatial information by gathering signal patterns and spatial dependence into an auxiliary statistic. 2d-SMT rejects the null when a primary statistic at the location of interest and the auxiliary statistic constructed based on nearby observations are greater than their corresponding cutoffs. 2d-SMT can also be combined with different variants of the weighted BH procedures to improve the detection power further. A fast algorithm is developed to accelerate the search for optimal cutoffs in 2d-SMT. In theory, we establish the asymptotic FDR control of 2d-SMT under weak spatial dependence. Extensive numerical experiments demonstrate that the 2d-SMT method combined with various weighted BH procedures achieves the most competitive performance in FDR and power trade-off. |
| title | Powerful Spatial Multiple Testing via Borrowing Neighboring Information |
| topic | Methodology |
| url | https://arxiv.org/abs/2210.17121 |