Department of Statistics, The Chinese University of Hong Kong, Hong Kong, 999077, China.
Department of Neurology and Neurological Sciences, Stanford University, Stanford, California, 94304, United States.
Biometrics. 2024 Jul 1;80(3). doi: 10.1093/biomtc/ujae082.
Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with family-wise error rate (FWER) control when only summary statistics of marginal dependence are accessible, we adopt GhostKnockoff to directly generate knockoff copies of summary statistics and propose a new filter to select features conditionally dependent on the response. In addition, we develop a computationally efficient algorithm to greatly reduce the computational cost of knockoff copies generation without sacrificing power and FWER control. Experiments on simulated data and a real dataset of Alzheimer's disease genetics demonstrate the advantage of the proposed method over existing alternatives in both statistical power and computational efficiency.
用可证明的错误率控制来检验多个条件独立性假设是一个具有多种应用的基本问题。为了在仅可访问边缘依赖性汇总统计信息的情况下进行控制错误发现率 (FWER) 的条件独立性推断，我们采用 GhostKnockoff 直接生成汇总统计信息的 Knockoff 副本，并提出一种新的滤波器来选择与响应条件相关的特征。此外，我们开发了一种计算效率高的算法，在不牺牲功效和 FWER 控制的情况下，大大降低了 Knockoff 副本生成的计算成本。在模拟数据和阿尔茨海默病遗传学的真实数据集上的实验表明，与现有替代方法相比，该方法在统计功效和计算效率方面都具有优势。
