Joint non-parametric estimation of mean and auto-covariances for Gaussian processes

Descripción del Articulo

Gaussian processes that can be decomposed into a smooth mean function and a stationary autocorrelated noise process are considered and a fully automatic nonparametric method to simultaneous estimation of mean and auto-covariance functions of such processes is developed. The proposed empirical Bayes...

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Detalles Bibliográficos
Autores: Krivobokova, Tatyana, Serra, Paulo, Rosales, Francisco, Klockmann, Karolina
Formato: artículo
Fecha de Publicación:2022
Institución:Universidad ESAN
Repositorio:ESAN-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.esan.edu.pe:20.500.12640/3299
Enlace del recurso:https://hdl.handle.net/20.500.12640/3299
https://doi.org/10.1016/j.csda.2022.107519
Nivel de acceso:acceso abierto
Materia:Demmler-Reinsch basis
Empirical Bayes
Spectral density
Stationary process
Base de Demmler-Reinsch
Bayes empírico
Densidad espectral
Proceso estacionario
https://purl.org/pe-repo/ocde/ford#2.11.00
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spelling Krivobokova, TatyanaSerra, PauloRosales, FranciscoKlockmann, Karolina2023-01-23T02:17:22Z2023-01-23T02:17:22Z2022-05-05Krivobokova, T., Serra, P., Rosales, F., & Klockmann, K. (2022). Joint non-parametric estimation of mean and auto-covariances for Gaussian processes. Computational Statistics and Data Analysis, 173(2022), 107519. https://doi.org/10.1016/j.csda.2022.107519https://hdl.handle.net/20.500.12640/3299https://doi.org/10.1016/j.csda.2022.107519Gaussian processes that can be decomposed into a smooth mean function and a stationary autocorrelated noise process are considered and a fully automatic nonparametric method to simultaneous estimation of mean and auto-covariance functions of such processes is developed. The proposed empirical Bayes approach is data-driven, numerically efficient, and allows for the construction of confidence sets for the mean function. Performance is demonstrated in simulations and real data analysis. The method is implemented in the R package eBsc.application/pdfInglésengElsevierInternational Association for Statistical ComputingComputational and Methodological StatisticsNLurn:issn:0167-94731urn:issn:1872-7352https://www.sciencedirect.com/science/article/pii/S0167947322000998/pdfft?md5=b4009fe50e464f6c2c4717b7b42e1541&pid=1-s2.0-S0167947322000998-main.pdfinfo:eu-repo/semantics/openAccessAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/Demmler-Reinsch basisEmpirical BayesSpectral densityStationary processBase de Demmler-ReinschBayes empíricoDensidad espectralProceso estacionariohttps://purl.org/pe-repo/ocde/ford#2.11.00Joint non-parametric estimation of mean and auto-covariances for Gaussian processesinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículoreponame:ESAN-Institucionalinstname:Universidad ESANinstacron:ESANhttps://orcid.org/0000-0003-2347-632XAcceso abiertoComputational Statistics and Data Analysis107519173ORIGINALrosales_2022.pdfrosales_2022.pdfTexto completoapplication/pdf694777https://repositorio.esan.edu.pe/bitstreams/07acb636-f7bf-4575-ae03-7f89f9bdf010/download649bf7610de2a221cdecd4fa140542c7MD51trueAnonymousREADTHUMBNAILrosales_2022.pdf.jpgrosales_2022.pdf.jpgGenerated Thumbnailimage/jpeg5457https://repositorio.esan.edu.pe/bitstreams/5ee9770d-b60c-4532-819e-ef5587048e6d/downloade412ad7ea20b292f130d47a8e66bd416MD55falseAnonymousREADTEXTrosales_2022.pdf.txtrosales_2022.pdf.txtExtracted texttext/plain67154https://repositorio.esan.edu.pe/bitstreams/07dd09b0-a38d-4bc1-b914-305aceac0e5d/downloadf6e3cbb158e9baea33f7b96985f824fdMD54falseAnonymousREAD20.500.12640/3299oai:repositorio.esan.edu.pe:20.500.12640/32992024-11-25 19:41:21.422https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessopen.accesshttps://repositorio.esan.edu.peRepositorio Institucional ESANrepositorio@esan.edu.pe
dc.title.en_EN.fl_str_mv Joint non-parametric estimation of mean and auto-covariances for Gaussian processes
title Joint non-parametric estimation of mean and auto-covariances for Gaussian processes
spellingShingle Joint non-parametric estimation of mean and auto-covariances for Gaussian processes
Krivobokova, Tatyana
Demmler-Reinsch basis
Empirical Bayes
Spectral density
Stationary process
Base de Demmler-Reinsch
Bayes empírico
Densidad espectral
Proceso estacionario
https://purl.org/pe-repo/ocde/ford#2.11.00
title_short Joint non-parametric estimation of mean and auto-covariances for Gaussian processes
title_full Joint non-parametric estimation of mean and auto-covariances for Gaussian processes
title_fullStr Joint non-parametric estimation of mean and auto-covariances for Gaussian processes
title_full_unstemmed Joint non-parametric estimation of mean and auto-covariances for Gaussian processes
title_sort Joint non-parametric estimation of mean and auto-covariances for Gaussian processes
author Krivobokova, Tatyana
author_facet Krivobokova, Tatyana
Serra, Paulo
Rosales, Francisco
Klockmann, Karolina
author_role author
author2 Serra, Paulo
Rosales, Francisco
Klockmann, Karolina
author2_role author
author
author
dc.contributor.author.fl_str_mv Krivobokova, Tatyana
Serra, Paulo
Rosales, Francisco
Klockmann, Karolina
dc.subject.en_EN.fl_str_mv Demmler-Reinsch basis
Empirical Bayes
Spectral density
Stationary process
topic Demmler-Reinsch basis
Empirical Bayes
Spectral density
Stationary process
Base de Demmler-Reinsch
Bayes empírico
Densidad espectral
Proceso estacionario
https://purl.org/pe-repo/ocde/ford#2.11.00
dc.subject.es_ES.fl_str_mv Base de Demmler-Reinsch
Bayes empírico
Densidad espectral
Proceso estacionario
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.11.00
description Gaussian processes that can be decomposed into a smooth mean function and a stationary autocorrelated noise process are considered and a fully automatic nonparametric method to simultaneous estimation of mean and auto-covariance functions of such processes is developed. The proposed empirical Bayes approach is data-driven, numerically efficient, and allows for the construction of confidence sets for the mean function. Performance is demonstrated in simulations and real data analysis. The method is implemented in the R package eBsc.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2023-01-23T02:17:22Z
dc.date.available.none.fl_str_mv 2023-01-23T02:17:22Z
dc.date.issued.fl_str_mv 2022-05-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.other.none.fl_str_mv Artículo
format article
status_str publishedVersion
dc.identifier.citation.none.fl_str_mv Krivobokova, T., Serra, P., Rosales, F., & Klockmann, K. (2022). Joint non-parametric estimation of mean and auto-covariances for Gaussian processes. Computational Statistics and Data Analysis, 173(2022), 107519. https://doi.org/10.1016/j.csda.2022.107519
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12640/3299
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.csda.2022.107519
identifier_str_mv Krivobokova, T., Serra, P., Rosales, F., & Klockmann, K. (2022). Joint non-parametric estimation of mean and auto-covariances for Gaussian processes. Computational Statistics and Data Analysis, 173(2022), 107519. https://doi.org/10.1016/j.csda.2022.107519
url https://hdl.handle.net/20.500.12640/3299
https://doi.org/10.1016/j.csda.2022.107519
dc.language.none.fl_str_mv Inglés
dc.language.iso.none.fl_str_mv eng
language_invalid_str_mv Inglés
language eng
dc.relation.ispartof.none.fl_str_mv urn:issn:0167-94731
urn:issn:1872-7352
dc.relation.uri.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S0167947322000998/pdfft?md5=b4009fe50e464f6c2c4717b7b42e1541&pid=1-s2.0-S0167947322000998-main.pdf
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.en.fl_str_mv Attribution 4.0 International
dc.rights.uri.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
International Association for Statistical Computing
Computational and Methodological Statistics
dc.publisher.country.none.fl_str_mv NL
publisher.none.fl_str_mv Elsevier
International Association for Statistical Computing
Computational and Methodological Statistics
dc.source.none.fl_str_mv reponame:ESAN-Institucional
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Joint non-parametric estimation of mean and auto-covariances for Gaussian processes
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