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dc.contributor.authorKazor, Karen
dc.contributor.authorHering, Amanda S.
dc.date.accessioned2022-06-06T10:41:35Z
dc.date.available2022-06-06T10:41:35Z
dc.date.issued2019-06-17
dc.identifier.citationKazor, K., & Hering, A. S. (2019). Mixture of Regression Models for Large Spatial Datasets. Technometrics, 61(4), 507–523. doi:10.1080/00401706.2019.1569558
dc.identifier.issn1537-2723
dc.identifier.issn0040-1706
dc.identifier.doi10.1080/00401706.2019.1569558
dc.identifier.urihttp://hdl.handle.net/10754/678688
dc.description.abstractWhen a spatial regression model that links a response variable to a set of explanatory variables is desired, it is unlikely that the same regression model holds throughout the domain when the spatial domain and dataset are both large and complex. The locations where the trend changes may not be known, and we present here a mixture of regression models approach to identifying the locations wherein the relationship between the predictors and the response is similar; to estimating the model within each group; and to estimating the number of groups. An EM algorithm for estimating this model is presented along with a criterion for choosing the number of groups. Performance of the estimators and model selection are demonstrated through simulation. An example with groundwater depth and associated predictors generated from a large physical model simulation demonstrates the fit and interpretation of the proposed model. R code is provided in the supplementary materials that simulates the scenarios tested herein; implements the method; and reproduces the groundwater depth results. Supplementary materials for this article are available online.
dc.description.sponsorshipThis research was partially supported by the National Science Foundation under Cooperative Agreement EEC-1028969 (ERC/ReNUWIt); the State of Colorado through the Higher Education Competitive Research Authority; and King Abdullah University of Science and Technology through CRG2015-2582.
dc.publisherAMER STATISTICAL ASSOC
dc.relation.urlhttps://www.tandfonline.com/doi/full/10.1080/00401706.2019.1569558
dc.subjectGroundwater
dc.subjectMarkov process
dc.subjectMixture of regression models
dc.subjectNonstationarity
dc.subjectSpatial trends
dc.titleMixture of Regression Models for Large Spatial Datasets
dc.typeArticle
dc.identifier.journalTECHNOMETRICS
dc.identifier.wosutWOS:000478161600001
dc.contributor.institutionColorado Sch Mines, Dept Appl Math & Stat, Golden, CO 80401 USA
dc.contributor.institutionBaylor Univ, Dept Stat Sci, One Bear Pl 97140, Waco, TX 76798 USA
dc.identifier.volume61
dc.identifier.issue4
dc.identifier.pages507-523
kaust.grant.numberCRG2015-2582
dc.identifier.eid2-s2.0-85067622208


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