Assumption-Lean Inference for Spectral Differential Network Analysis of High-Dimensional Time Series
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
The Flow has not summarised this story yet — read it at arXiv Statistics ML.
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Forecasting time-varying functional connectivity from electroencephalography (EEG) requires modeling both history-dependent trends and structured variability across channels. Conditional flow matching...
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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.
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