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Joint estimation of precision matrices for long-memory time series

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  • Zhang, Qihu
  • Chung, Jongik
  • Park, Cheolwoo

Abstract

Methods are proposed for estimating multiple precision matrices for long-memory time series, with particular emphasis on the analysis of resting-state functional magnetic resonance imaging (fMRI) data obtained from multiple subjects. The objective is to estimate both individual brain networks and a common structure representative of a group. Several approaches employing weighted aggregation are introduced to simultaneously estimate individual and group-level precision matrices. Convergence rates of the estimators are examined under various norms and expectations, and their performance is evaluated under both sub-Gaussian and heavy-tailed distributions. The proposed methods are demonstrated through simulated data and real resting-state fMRI datasets.

Suggested Citation

  • Zhang, Qihu & Chung, Jongik & Park, Cheolwoo, 2025. "Joint estimation of precision matrices for long-memory time series," Computational Statistics & Data Analysis, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:csdana:v:212:y:2025:i:c:s0167947325001100
    DOI: 10.1016/j.csda.2025.108234
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