Cluster detection based on spatial associations and iterated residuals in generalized linear mixed models.
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.
Department of Statistics, Purdue University, West Lafayette, Indiana 47907-2066, USA. tlzhang@stat.purdue.edu
Tonglin
T
Zhang
Ge
G
Lin
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015340:Epidemiologic Research Design; D016014:Linear Models; D010363:Pattern Recognition, Automated; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity