2022/08/27 by J. Jenny Li, Li, Jiaqi, Likai Chen +5
Computer Science · Decision Sciences · Medicine · #Advanced Statistical Process Monitoring #Data-Driven Disease Surveillance #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Statistics Theory (math.ST) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2208.13074
openalex publication_date 2022/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose an inference method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. Our method aggregates moving sum (MOSUM) statistics cross-sectionally by an ℓ2-norm and maximizes them over time. We further introduce a novel Two-Way MOSUM, which utilizes spatial-temporal moving regions to search for breaks, with the added advantage of enhancing testing power when breaks occur in only a few groups. The limiting distribution of an ℓ2-aggregated statistic is established for testing break existence by extending a high-dimensional Gaussian approximation theorem to spatial-temporal non-stationary processes. Simulation studies exhibit promising performance of our test in detecting non-sparse weak signals. Two applications, analyzing equity returns and COVID-19 cases in the United States, showcase the real-world relevance of our proposed algorithms.