Variance Estimation and Kriging Prediction for a Class of Non-Stationary Spatial Models Yang, Shu Zhu, Zhengyuan Zhu, Zhengyuan
dc.contributor.department Statistics 2018-03-22T17:00:19.000 2020-07-02T06:56:43Z 2020-07-02T06:56:43Z Thu Jan 01 00:00:00 UTC 2015 2015-01-01
dc.description.abstract <p>This paper discusses the estimation and plug-in kriging prediction non-stationary spatial process assuming a smoothly varying variance an additive independent measurement error. A difference-based kernel estimator of the variance function and a modified likelihood estimator of the mea surement error variance are used for parameter estimation. Asymptotic properties of these estimators and the plug-in kriging predictor are established. A simula tion study is presented to test our estimation-prediction procedure. Our kriging predictor is shown to perform better than the spatial adaptive local polynomial regression estimator proposed by Fan and Gijbels (1995) when the measurement error is small.</p>
dc.description.comments <p>This article is published as Shu Yang and Zhengyuan Zhu, "Variance Estimation and Kriging Prediction for a Class of Non-stationary Spatial Models," <em>Statistica Sinica </em>25(1), (2015): 135-149. DOI: <a href="" target="_blank">10.5705/ss.2013.205w</a>. Posted with permission.</p>
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dc.identifier archive/
dc.identifier.articleid 1138
dc.identifier.contextkey 11819644
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/131
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 19:44:34 UTC 2022
dc.source.uri 10.5705/ss.2013.205w
dc.subject.disciplines Multivariate Analysis
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Bandwidth selection
dc.subject.keywords heteroscedasticity
dc.subject.keywords K-fold cross validation
dc.subject.keywords local polynomial regression
dc.subject.keywords rates of convergence
dc.subject.keywords variance function
dc.title Variance Estimation and Kriging Prediction for a Class of Non-Stationary Spatial Models
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 51db2a08-8f9d-4f97-bdbc-6790b3d5a608
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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