Asynchronous Changepoint Estimation for Spatially Correlated Functional Time Series

ABSTRACT: We propose a new solution under the Bayesian framework to simultaneously estimate mean-based asynchronous changepoints in spatially correlated functional time series. Unlike previous methods that assume a shared changepoint at all spatial locations or ignore spatial correlation, our method treats changepoints as a spatial process. This allows our model to respect spatial heterogeneity and exploit spatial correlations to improve estimation. Our method is derived from the ubiquitous cumulative sum (CUSUM) statistic that dominates changepoint detection in functional time series. However, instead of directly searching for the maximum of the CUSUM-based processes, we build spatially correlated two-piece linear models with appropriate variance structure to locate all changepoints at once. The pr

SUBMITTER: Wang M 

PROVIDER: S-EPMC9579602 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature