A0286
Title: Simultaneously detecting spatiotemporal changes with penalized Poisson regression models
Authors: Xin Wang - San Diego State University (United States) [presenting]
Abstract: In the realm of large-scale spatiotemporal data, abrupt changes are commonly occurring across both spatial and temporal domains. The aim is to address the concurrent challenges of detecting change points and identifying spatial clusters within spatiotemporal count data. An innovative method based on the Poisson regression model is introduced, employing doubly fused penalization to unveil the underlying spatiotemporal change patterns. To efficiently estimate the model, an iterative shrinkage and threshold-based algorithm to minimize the doubly penalized likelihood function is presented. The statistical consistency properties of the proposed estimator confirm its reliability and accuracy. Furthermore, extensive numerical experiments are conducted to validate the theoretical findings, thereby highlighting the superior performance of the method when compared to existing competitive approaches.
