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Cluster Detection Based on Spatial Associations and Iterated Residuals in Generalized Linear Mixed Models
Author(s) -
Zhang Tonglin,
Lin Ge
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01069.x
Subject(s) - frequentist inference , generalized linear mixed model , generalized linear model , spatial analysis , bayesian probability , cluster analysis , spatial dependence , spatial econometrics , cluster (spacecraft) , computer science , iterated function , mathematics , algorithm , statistics , data mining , bayesian inference , econometrics , mathematical analysis , programming language
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.