Joint estimation of precision matrices for long-memory time series

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.