High-dimensional Gaussian Graphical Model Testing for Long-Memory Time Series

The paper introduces a data‑adaptive test statistic for evaluating conditional independence in the graph structure of stationary Gaussian time series, addressing both short‑memory and long‑memory regimes. It provides a finite‑sample Berry–Esseen type Gaussian approximation and validates the testing procedure via block bootstrap, even in ultra‑high‑dimensional settings. The authors also propose a consistency‑enhancing correction, achieving asymptotic consistency in size and power, and demonstrate the method on fMRI data to explore brain functional connectivity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.