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dc.contributor.authorWang, Wu
dc.contributor.authorSun, Ying
dc.date.accessioned2019-04-29T07:52:43Z
dc.date.available2019-04-29T07:52:43Z
dc.date.issued2019-04-25
dc.identifier.citationWang W, Sun Y (2019) Penalized Local Polynomial Regression for Spatial Data. Biometrics. Available: http://dx.doi.org/10.1111/biom.13077.
dc.identifier.issn0006-341X
dc.identifier.issn1541-0420
dc.identifier.doi10.1111/biom.13077
dc.identifier.urihttp://hdl.handle.net/10754/632545
dc.description.abstractWhen performing spatial regression analysis in environmental data applications, spatial heterogeneity in the regression coefficients is often observed. Spatially varying coefficient models, including geographically weighted regression and spline models, are standard tools for quantifying such heterogeneity. In this paper, we propose a spatially varying coefficient model that represents the spatially varying parameters as a mixture of local polynomials at selected locations. The local polynomial parameters have attractive interpretations, indicating various types of spatial heterogeneity. Instead of estimating the spatially varying regression coefficients directly, we develop a penalized least squares regression procedure for the local polynomial parameter estimation, which both shrinks the parameter estimation and penalizes the differences among parameters that are associated with neighboring locations. We develop confidence intervals for the varying regression coefficients and prediction intervals for the response. We apply the proposed method to characterize the spatially varying association between particulate matter concentrations (PM2.5 ) and pollutant gases related to the secondary aerosol formulation in China. The identified regression coefficients show distinct spatial patterns for nitrogen dioxide, sulfur dioxide, and carbon monoxide during different seasons. This article is protected by copyright. All rights reserved.
dc.description.sponsorshipWe thank the associate editor and a referee for their help in improving the paper. This research was supported by funding from King Abdullah University of Science and Technology (KAUST).
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13077
dc.rightsArchived with thanks to Biometrics
dc.subjectSpatial Regression
dc.subjectFused Lasso
dc.subjectFine Particular Matter
dc.subjectSpatially Varying Coefficient Model
dc.titlePenalized Local Polynomial Regression for Spatial Data
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalBiometrics
dc.eprint.versionPost-print
kaust.personWang, Wu
kaust.personSun, Ying
refterms.dateFOA2019-04-29T08:47:34Z


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