无模型高维中介变量选择与错误发现率控制
Model-free High Dimensional Mediator Selection with False Discovery Rate Control
AI总结：
针对高维中介变量具有复杂相关结构的选择难题，本文将其转化为复合零假设检验，提出一种温和假设下的FDR控制方法，理论证明与模拟验证了其效力和性能，并在ADNI数据中成功识别出关键脑区中介变量。
摘要：
当中介变量具有复杂的相关结构和交互作用时，选择高维中介变量面临挑战。在本研究中，我们将高维中介变量选择问题转化为一系列具有复合零假设的假设检验，并开发了一种在中介模型上具有温和假设的错误发现率（FDR）控制方法。我们证明了所提出的方法和算法实现FDR控制的理论保证。我们展示了广泛的模拟结果，以证明与现有方法相比的检验效力和有限样本性能。最后，我们展示了该方法在分析阿尔茨海默病神经影像学计划（ADNI）数据中的应用，其中所提出的方法选择了海马体和杏仁核的体积，以及一些其他重要的MRI衍生测量指标，作为性别与痴呆进展之间关系的中介变量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
