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

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  • Yumou Qiu
  • Xiao-Hua Zhou

Abstract

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.

Suggested Citation

  • Yumou Qiu & Xiao-Hua Zhou, 2022. "Inference on Multi-level Partial Correlations Based on Multi-subject Time Series Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(540), pages 2268-2282, October.
    • Handle: RePEc:taf:jnlasa:v:117:y:2022:i:540:p:2268-2282
    DOI: 10.1080/01621459.2021.1917417
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