Structural Identification for Spatio-Temporal Dynamic Models
提出一种基于小空间块差异度量的新方法，用于识别动态空间数据中的潜在聚类结构和变化边界，适用于非平稳和不规则采样数据，并建立了渐近性质。
Identifying latent cluster structures in spatial trends constitutes an important yet challenging task in diverse applications. In this paper, we propose a novel method based on a discrepancy measure over small spatial blocks that effectively uncovers heterogeneity within dynamic spatial data. Our approach effectively detects boundaries where structural changes occur, thus allowing for more nuanced insights into underlying spatial patterns. Unlike methods predicated on strong stationarity assumptions, our framework accommodates piecewise-defined parameters and irregular sampling locations, enabling its broad applicability to real-world datasets. We further establish asymptotic properties and limit distributions of the proposed methods by leveraging the notion of spatial physical dependence, accounting for correlations across spatial domains. Simulations and real data analyses confirm the effectiveness of the method, highlighting its robustness and accuracy in identifying complex spatio-temporal structures.