Handle URI:
http://hdl.handle.net/10754/597807
Title:
Comparing Spatial Predictions
Authors:
Hering, Amanda S.; Genton, Marc G.
Abstract:
Under 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.
Citation:
Hering AS, Genton MG (2011) Comparing Spatial Predictions. Technometrics 53: 414–425. Available: http://dx.doi.org/10.1198/TECH.2011.10136.
Publisher:
Informa UK Limited
Journal:
Technometrics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Nov-2011
DOI:
10.1198/TECH.2011.10136
Type:
Article
ISSN:
0040-1706; 1537-2723
Sponsors:
This 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.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorHering, Amanda S.en
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2016-02-25T12:57:03Zen
dc.date.available2016-02-25T12:57:03Zen
dc.date.issued2011-11en
dc.identifier.citationHering AS, Genton MG (2011) Comparing Spatial Predictions. Technometrics 53: 414–425. Available: http://dx.doi.org/10.1198/TECH.2011.10136.en
dc.identifier.issn0040-1706en
dc.identifier.issn1537-2723en
dc.identifier.doi10.1198/TECH.2011.10136en
dc.identifier.urihttp://hdl.handle.net/10754/597807en
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.en
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.en
dc.publisherInforma UK Limiteden
dc.subjectHypothesis testen
dc.subjectKrigingen
dc.subjectLoss functionsen
dc.subjectModel validationen
dc.subjectPrediction evaluationen
dc.subjectWind poweren
dc.titleComparing Spatial Predictionsen
dc.typeArticleen
dc.identifier.journalTechnometricsen
dc.contributor.institutionColorado School of Mines, Golden, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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