Joint estimation of precision matrices for long-memory time series

Qihu Zhang, Jongik Chung and Cheolwoo Park

Computational Statistics & Data Analysis, 2025, vol. 212, issue C

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.

Keywords: Aggregation; Brain functional connectivity; High-dimensional data; Long-memory time series; Precision matrix estimation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:212:y:2025:i:c:s0167947325001100

DOI: 10.1016/j.csda.2025.108234

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