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

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dc.contributor.authorKrivobokova, Tatyana
dc.contributor.authorSerra, Paulo
dc.contributor.authorRosales, Francisco
dc.contributor.authorKlockmann, Karolina
dc.date.accessioned2023-01-23T02:17:22Z
dc.date.available2023-01-23T02:17:22Z
dc.date.issued2022-05-05
dc.description.abstractGaussian 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.en_EN
dc.formatapplication/pdf
dc.identifier.citationKrivobokova, 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.doihttps://doi.org/10.1016/j.csda.2022.107519
dc.identifier.urihttps://hdl.handle.net/20.500.12640/3299
dc.language.isoeng
dc.publisherElsevier
dc.publisherInternational Association for Statistical Computing
dc.publisherComputational and Methodological Statistics
dc.publisher.countryNL
dc.relation.ispartofurn:issn:0167-94731
dc.relation.ispartofurn:issn:1872-7352
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0167947322000998/pdfft?md5=b4009fe50e464f6c2c4717b7b42e1541&pid=1-s2.0-S0167947322000998-main.pdf
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDemmler-Reinsch basisen_EN
dc.subjectEmpirical Bayesen_EN
dc.subjectBase de Demmler-Reinsches_ES
dc.subjectSpectral densityen_EN
dc.subjectBayes empíricoes_ES
dc.subjectDensidad espectrales_ES
dc.subjectStationary processen_EN
dc.subjectProceso estacionarioes_ES
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.11.00
dc.titleJoint non-parametric estimation of mean and auto-covariances for Gaussian processesen_EN
dc.typeinfo:eu-repo/semantics/article
dc.type.otherArtículo
dc.type.versioninfo:eu-repo/semantics/publishedVersion
local.author.orcidhttps://orcid.org/0000-0003-2347-632X
oaire.citation.startPage107519
oaire.citation.titleComputational Statistics and Data Analysis
oaire.citation.volume173
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