Spatial cluster detection of regression coefficients in a mixed-effects model
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2019-05-22
Print Publication Date2020-03
Embargo End Date2020-05-22
Permanent link to this recordhttp://hdl.handle.net/10754/655987
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AbstractIdentifying spatial clusters of different regression coefficients is a useful tool for discerning the distinctive relationship between a response and covariates in space. Most of the existing cluster detection methods aim to identify the spatial similarity in responses, and the standard cluster detection algorithm assumes independent spatial units. However, the response variables are spatially correlated in many environmental applications. We propose a mixed-effects model for spatial cluster detection that takes spatial correlation into account. Compared to a fixed-effects model, the introduced random effects explain extra variability among the spatial responses beyond the cluster effect, thus reducing the false positive rate. The developed method exploits a sequential searching scheme and is able to identify multiple potentially overlapping clusters. We use simulation studies to evaluate the performance of our proposed method in terms of the true and false positive rates of a known cluster and the identification of multiple known clusters. We apply our proposed methodology to particulate matter (PM2.5) concentration data from the Northeastern United States in order to study the weather effect on PM2.5 and to investigate the association between the simulations from a numerical model and the satellite-derived aerosol optical depth data. We find geographical hot spots that show distinct features, comparing to the background.
CitationLee, J., Sun, Y., & Chang, H. H. (2019). Spatial cluster detection of regression coefficients in a mixed-effects model. Environmetrics, 31(2). doi:10.1002/env.2578
SponsorsThe authors thank the editor, an associate editor, and two referees for their insightful and constructive comments. The research reported in this publication was supported by King Abdullah University of Science and Technology and by National Institutes of Health Grant R01 ES027892. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank Dr. James Mulholland, Dr. Armistead Russell, Dr. Yang Liu, and Ms. Niru Senthikumar for providing the CMAQ and satellite data.