2023/02/10 by Lujia Bai, Bai, Lujia, Wei‐Chi Wu +1
Biochemistry, Genetics and Molecular Biology · Neuroscience · Psychology · #FOS: Computer and information sciences #FOS: Mathematics #Functional Brain Connectivity Studies #Gene Regulatory Network Analysis #Mental Health Research Topics #Methodology (stat.ME) #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2302.05158
openalex publication_date 2023/02/10 · openalex created_date 2023/02/14 · openalex updated_date 2026/07/28
This paper proposes a flexible framework for inferring large-scale time-varying and time-lagged correlation networks from multivariate or high-dimensional non-stationary time series with piecewise smooth trends. Built on a novel and unified multiple-testing procedure of time-lagged cross-correlation functions with a fixed or diverging number of lags, our method can accurately disclose flexible time-varying network structures associated with complex functional structures at all time points. We broaden the applicability of our method to the structure breaks by developing difference-based nonparametric estimators of cross-correlations, achieve accurate family-wise error control via a bootstrap-assisted procedure adaptive to the complex temporal dynamics, and enhance the probability of recovering the time-varying network structures using a new uniform variance reduction technique. We prove the asymptotic validity of the proposed method and demonstrate its effectiveness in finite samples through simulation studies and empirical applications.