Model-free High Dimensional Mediator Selection with False Discovery Rate Control
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912586912497664 |
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| author | Wang, Runqiu Dai, Ran Wang, Jieqiong Soh, Kah Meng Xu, Ziyang Azzam, Mohamed Dai, Hongying Zheng, Cheng |
| author_facet | Wang, Runqiu Dai, Ran Wang, Jieqiong Soh, Kah Meng Xu, Ziyang Azzam, Mohamed Dai, Hongying Zheng, Cheng |
| contents | There is a challenge in selecting high-dimensional mediators when the mediators have complex correlation structures and interactions. In this work, we frame the high-dimensional mediator selection problem into a series of hypothesis tests with composite nulls, and develop a method to control the false discovery rate (FDR) which has mild assumptions on the mediation model. We show the theoretical guarantee that the proposed method and algorithm achieve FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with existing methods. Lastly, we demonstrate the method for analyzing the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, in which the proposed method selects the volume of the hippocampus and amygdala, as well as some other important MRI-derived measures as mediators for the relationship between gender and dementia progression. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09105 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model-free High Dimensional Mediator Selection with False Discovery Rate Control Wang, Runqiu Dai, Ran Wang, Jieqiong Soh, Kah Meng Xu, Ziyang Azzam, Mohamed Dai, Hongying Zheng, Cheng Methodology There is a challenge in selecting high-dimensional mediators when the mediators have complex correlation structures and interactions. In this work, we frame the high-dimensional mediator selection problem into a series of hypothesis tests with composite nulls, and develop a method to control the false discovery rate (FDR) which has mild assumptions on the mediation model. We show the theoretical guarantee that the proposed method and algorithm achieve FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with existing methods. Lastly, we demonstrate the method for analyzing the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, in which the proposed method selects the volume of the hippocampus and amygdala, as well as some other important MRI-derived measures as mediators for the relationship between gender and dementia progression. |
| title | Model-free High Dimensional Mediator Selection with False Discovery Rate Control |
| topic | Methodology |
| url | https://arxiv.org/abs/2505.09105 |