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An approach for valid covariance estimation via the Fourier series

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Resumo:The use of kriging for construction of prediction or risk maps requires estimating the dependence structure of the random process, which can be addressed through the approximation of the covariance function. The nonparametric estimators used for the latter aim are not necessarily valid to solve the kriging system, since the positive-definiteness condition of the covariance estimator typically fails. The usage of a parametric covariance instead may be attractive at first because of its simplicity, although it may be affected by misspecification. An alternative is suggested in this paper to obtain a valid covariance from a nonparametric estimator through the Fourier series tool, which involves two issues: estimation of the Fourier coefficients and selection of the truncation point to determine the number of terms in the Fourier expansion. Numerical studies for simulated data have been conducted to illustrate the performance of this approach. In addition, an application to a real environmental data set is included, related to the presence of nitrate in groundwater in Beja district (Portugal), so that pollution maps of the region are generated by solving the kriging equations with the use of the Fourier series estimates of the covariance.
Autores principais:García Soidán, Pilar
Outros Autores:Menezes, Raquel; Rubinos-Lopez, Oscar
Assunto:Covariance function Fourier series Kriging Truncation point
Ano:2012
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso restrito
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho
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author García Soidán, Pilar
author2 Menezes, Raquel
Rubinos-Lopez, Oscar
author2_role author
author
author_facet García Soidán, Pilar
Menezes, Raquel
Rubinos-Lopez, Oscar
author_role author
contributor_name_str_mv Universidade do Minho
country_str PT
creators_json_txt [{\"Person.name\":\"García Soidán, Pilar\"},{\"Person.name\":\"Menezes, Raquel\"},{\"Person.name\":\"Rubinos-Lopez, Oscar\"}]
datacite.contributors.contributor.contributorName.fl_str_mv Universidade do Minho
datacite.creators.creator.creatorName.fl_str_mv García Soidán, Pilar
Menezes, Raquel
Rubinos-Lopez, Oscar
datacite.date.Accepted.fl_str_mv 2012-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2011-07-26T16:31:21Z
datacite.date.embargoed.fl_str_mv 2011-07-26T16:31:21Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_16ec
datacite.subjects.subject.fl_str_mv Covariance function
Fourier series
Kriging
Truncation point
datacite.titles.title.fl_str_mv An approach for valid covariance estimation via the Fourier series
dc.contributor.none.fl_str_mv Universidade do Minho
dc.creator.none.fl_str_mv García Soidán, Pilar
Menezes, Raquel
Rubinos-Lopez, Oscar
dc.date.Accepted.fl_str_mv 2012-01-01T00:00:00Z
dc.date.available.fl_str_mv 2011-07-26T16:31:21Z
dc.date.embargoed.fl_str_mv 2011-07-26T16:31:21Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv https://hdl.handle.net/1822/13067
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv Springer
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.subject.none.fl_str_mv Covariance function
Fourier series
Kriging
Truncation point
dc.title.fl_str_mv An approach for valid covariance estimation via the Fourier series
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_6501
description The use of kriging for construction of prediction or risk maps requires estimating the dependence structure of the random process, which can be addressed through the approximation of the covariance function. The nonparametric estimators used for the latter aim are not necessarily valid to solve the kriging system, since the positive-definiteness condition of the covariance estimator typically fails. The usage of a parametric covariance instead may be attractive at first because of its simplicity, although it may be affected by misspecification. An alternative is suggested in this paper to obtain a valid covariance from a nonparametric estimator through the Fourier series tool, which involves two issues: estimation of the Fourier coefficients and selection of the truncation point to determine the number of terms in the Fourier expansion. Numerical studies for simulated data have been conducted to illustrate the performance of this approach. In addition, an application to a real environmental data set is included, related to the presence of nitrate in groundwater in Beja district (Portugal), so that pollution maps of the region are generated by solving the kriging equations with the use of the Fourier series estimates of the covariance.
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eu_rights_str_mv restrictedAccess
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fulltext.url.fl_str_mv https://prod-dspace.uminho.pt/bitstreams/b4ca3211-4bda-4813-8ba1-4d4035a7edd0/download
id rum_c0914aa4b07c37ed13f75203c00a28d2
identifier.url.fl_str_mv https://hdl.handle.net/1822/13067
instacron_str repositorium
institution Universidade do Minho
instname_str Universidade do Minho
language eng
network_acronym_str rum
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oai_identifier_str oai:repositorium.uminho.pt:1822/13067
organization_str_mv urn:organizationAcronym:repositorium
person_str_mv García Soidán, Pilar
Menezes, Raquel
Rubinos-Lopez, Oscar
publishDate 2012
publisher.none.fl_str_mv Springer
reponame_str RepositóriUM - Universidade do Minho
repository_id_str urn:repositoryAcronym:rum
service_str_mv urn:repositoryAcronym:rum
spelling engSpringerporThe use of kriging for construction of prediction or risk maps requires estimating the dependence structure of the random process, which can be addressed through the approximation of the covariance function. The nonparametric estimators used for the latter aim are not necessarily valid to solve the kriging system, since the positive-definiteness condition of the covariance estimator typically fails. The usage of a parametric covariance instead may be attractive at first because of its simplicity, although it may be affected by misspecification. An alternative is suggested in this paper to obtain a valid covariance from a nonparametric estimator through the Fourier series tool, which involves two issues: estimation of the Fourier coefficients and selection of the truncation point to determine the number of terms in the Fourier expansion. Numerical studies for simulated data have been conducted to illustrate the performance of this approach. In addition, an application to a real environmental data set is included, related to the presence of nitrate in groundwater in Beja district (Portugal), so that pollution maps of the region are generated by solving the kriging equations with the use of the Fourier series estimates of the covariance.application/pdfporAn approach for valid covariance estimation via the Fourier seriesGarcía Soidán, PilarMenezes, RaquelRubinos-Lopez, OscarHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISSNIsPartOf1866-6280ISSNIsPartOf1866-6299DOIIsPartOf10.1007/s12665-011-1269-42011-07-26T16:31:21Z20122012-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/13067http://purl.org/coar/access_right/c_16ecrestricted accessCovariance functionFourier seriesKrigingTruncation point1840257 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/b4ca3211-4bda-4813-8ba1-4d4035a7edd0/download
spellingShingle An approach for valid covariance estimation via the Fourier series
García Soidán, Pilar
Covariance function
Fourier series
Kriging
Truncation point
status SINGLETON
subject.fl_str_mv Covariance function
Fourier series
Kriging
Truncation point
title An approach for valid covariance estimation via the Fourier series
title_full An approach for valid covariance estimation via the Fourier series
title_fullStr An approach for valid covariance estimation via the Fourier series
title_full_unstemmed An approach for valid covariance estimation via the Fourier series
title_short An approach for valid covariance estimation via the Fourier series
title_sort An approach for valid covariance estimation via the Fourier series
topic Covariance function
Fourier series
Kriging
Truncation point
topic_facet Covariance function
Fourier series
Kriging
Truncation point
url https://hdl.handle.net/1822/13067
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