Simultaneously detecting spatiotemporal changes with penalized Poisson regression models
arxiv(2024)
Abstract
In the realm of large-scale spatiotemporal data, abrupt changes are commonly
occurring across both spatial and temporal domains. This study aims to address
the concurrent challenges of detecting change points and identifying spatial
clusters within spatiotemporal count data. We introduce an innovative method
based on the Poisson regression model, employing doubly fused penalization to
unveil the underlying spatiotemporal change patterns. To efficiently estimate
the model, we present an iterative shrinkage and threshold based algorithm to
minimize the doubly penalized likelihood function. We establish the statistical
consistency properties of the proposed estimator, confirming its reliability
and accuracy. Furthermore, we conduct extensive numerical experiments to
validate our theoretical findings, thereby highlighting the superior
performance of our method when compared to existing competitive approaches.
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