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Erschienen in:
2016 | OriginalPaper | Buchkapitel
A Statistical Method for Change-Set Analysis
verfasst von : Pei-Sheng Lin, Jun Zhu, Shu-Fu Kuo, Katherine Curtis
Erschienen in: Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics
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Abstract
In many scientific studies, it is of interest to group spatial units on a lattice with similar characteristics within a group but with distinction among groups. Here we develop a novel change-set method for this purpose, as a substantive extension of the existing change-point analysis for one-dimensional data in space or time. Our method addresses unique challenges resulting from the multi-dimensional space such as changes that occur abruptly in space and change sets of arbitrary shapes. In particular, we propose an entropy measure and establish quasi-likelihood estimation that accounts for covariates via change-set regression and spatial correlation via working covariance. For illustration, our method is applied to analyze a county-based socio-economic data set.