Inference on Multi-level Partial Correlations Based on Multi-subject Time Series Data

• 成果类型: Article
• 署名作者: Qiu, Yumou; Zhou, Xiao-Hua
• 署名单位: Iowa State University; Peking University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1917417
• 发表日期: 2022
• 页码: 2268-2282
• 关键词: sparse; covariance; connectivity; selection; networks; attention; default; MODEL

摘要

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.