Cluster detection based on spatial associations and iterated residuals in generalized linear mixed models |
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Authors: | Zhang Tonglin Lin Ge |
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Affiliation: | Department of Statistics, Purdue University, 250 North University Street, West Lafayette, Indiana 47907-2066, U.S.A.;Department of Geology and Geography, West Virginia University, Morgantown, West Virginia 26506-6800, U.S.A. |
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Abstract: | Summary . Spatial clustering is commonly modeled by a Bayesian method under the framework of generalized linear mixed effect models (GLMMs). Spatial clusters are commonly detected by a frequentist method through hypothesis testing. In this article, we provide a frequentist method for assessing spatial properties of GLMMs. We propose a strategy that detects spatial clusters through parameter estimates of spatial associations, and assesses spatial aspects of model improvement through iterated residuals. Simulations and a case study show that the proposed method is able to consistently and efficiently detect the locations and magnitudes of spatial clusters. |
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Keywords: | Generalized linear models Local clusters Mixed effects Moran's I statistic Pearson residuals Spatial heterogeneity |
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