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dc.contributor.authorHering, Amanda S.
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2016-02-25T12:57:03Z
dc.date.available2016-02-25T12:57:03Z
dc.date.issued2011-11
dc.identifier.citationHering AS, Genton MG (2011) Comparing Spatial Predictions. Technometrics 53: 414–425. Available: http://dx.doi.org/10.1198/TECH.2011.10136.
dc.identifier.issn0040-1706
dc.identifier.issn1537-2723
dc.identifier.doi10.1198/TECH.2011.10136
dc.identifier.urihttp://hdl.handle.net/10754/597807
dc.description.abstractUnder a general loss function, we develop a hypothesis test to determine whether a significant difference in the spatial predictions produced by two competing models exists on average across the entire spatial domain of interest. The null hypothesis is that of no difference, and a spatial loss differential is created based on the observed data, the two sets of predictions, and the loss function chosen by the researcher. The test assumes only isotropy and short-range spatial dependence of the loss differential but does allow it to be non-Gaussian, non-zero-mean, and spatially correlated. Constant and nonconstant spatial trends in the loss differential are treated in two separate cases. Monte Carlo simulations illustrate the size and power properties of this test, and an example based on daily average wind speeds in Oklahoma is used for illustration. Supplemental results are available online. © 2011 American Statistical Association and the American Society for Qualitys.
dc.description.sponsorshipThis research was partially supported by NSF grants DMS-1007504, CMG-0621118, and Award No. KUS-C1-016-04,made by King Abdullah University of Science and Technology(KAUST). The authors also thank the editor, associate editor,and two anonymous reviewers whose constructive commentshave greatly improved the presentation of the article.
dc.publisherInforma UK Limited
dc.subjectHypothesis test
dc.subjectKriging
dc.subjectLoss functions
dc.subjectModel validation
dc.subjectPrediction evaluation
dc.subjectWind power
dc.titleComparing Spatial Predictions
dc.typeArticle
dc.identifier.journalTechnometrics
dc.contributor.institutionColorado School of Mines, Golden, United States
dc.contributor.institutionTexas A and M University, College Station, United States
kaust.grant.numberKUS-C1-016-04


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