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

• Published in: Journal of the American Statistical Association Vol. 117; no. 540; pp. 2268 - 2282
• Main Authors: Qiu, Yumou, Zhou, Xiao-Hua
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 02.10.2022; Taylor & Francis Ltd
• Subjects: Adaptive control; Algorithms; brain; Brain functional connectivity; fMRI; Functional magnetic resonance imaging; High dimensionality; Parkinson's disease; Partial correlation; Regression analysis; Simulation; Statistical methods; Statistics; Time series; time series analysis; Time-dependent data
• ISSN: 0162-1459; 1537-274X; 1537-274X
• DOI: 10.1080/01621459.2021.1917417

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.