Inference on Multi-level Partial Correlations Based on Multi-subject Time Series Data
提出一个分层模型，同时分析个体和群体层面的偏相关关系，并开发了适用于时间依赖数据的多重检验程序，用于识别非零偏相关，在帕金森病脑功能连接数据中展示了应用。
Partial correlations are commonly used to analyze the conditional dependence among variables. In this work, we propose a hierarchical model to study both the subject- and population-level partial correlations based on multi-subject time-series data. Multiple testing procedures adaptive to temporally dependent data with false discovery proportion control are proposed to identify the nonzero partial correlations in both the subject and population levels. A computationally feasible algorithm is developed. Theoretical results and simulation studies demonstrate the good properties of the proposed procedures. We illustrate the application of the proposed methods in a real example of brain connectivity on fMRI data from normal healthy persons and patients with Parkinson’s disease. Supplementary materials for this article are available online.