Joint non-parametric estimation of mean and auto-covariances for Gaussian processes
dc.contributor.author | Krivobokova, Tatyana | |
dc.contributor.author | Serra, Paulo | |
dc.contributor.author | Rosales, Francisco | |
dc.contributor.author | Klockmann, Karolina | |
dc.date.accessioned | 2023-01-23T02:17:22Z | |
dc.date.available | 2023-01-23T02:17:22Z | |
dc.date.issued | 2022-05-05 | |
dc.description.abstract | 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. | en_EN |
dc.format | application/pdf | |
dc.identifier.citation | 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.doi | https://doi.org/10.1016/j.csda.2022.107519 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12640/3299 | |
dc.language | Inglés | |
dc.language.iso | eng | |
dc.publisher | Elsevier | |
dc.publisher | International Association for Statistical Computing | |
dc.publisher | Computational and Methodological Statistics | |
dc.publisher.country | NL | |
dc.relation.ispartof | urn:issn:0167-94731 | |
dc.relation.ispartof | urn:issn:1872-7352 | |
dc.relation.uri | https://www.sciencedirect.com/science/article/pii/S0167947322000998/pdfft?md5=b4009fe50e464f6c2c4717b7b42e1541&pid=1-s2.0-S0167947322000998-main.pdf | |
dc.rights | info:eu-repo/semantics/openAccess | * |
dc.rights | Attribution 4.0 International | en |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Demmler-Reinsch basis | en_EN |
dc.subject | Empirical Bayes | en_EN |
dc.subject | Base de Demmler-Reinsch | es_ES |
dc.subject | Spectral density | en_EN |
dc.subject | Bayes empírico | es_ES |
dc.subject | Densidad espectral | es_ES |
dc.subject | Stationary process | en_EN |
dc.subject | Proceso estacionario | es_ES |
dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#2.11.00 | |
dc.title | Joint non-parametric estimation of mean and auto-covariances for Gaussian processes | en_EN |
dc.type | info:eu-repo/semantics/article | |
dc.type.other | Artículo | |
dc.type.version | info:eu-repo/semantics/publishedVersion | |
local.acceso.esan | Acceso abierto | |
local.author.orcid | https://orcid.org/0000-0003-2347-632X | |
oaire.citation.startPage | 107519 | |
oaire.citation.title | Computational Statistics and Data Analysis | |
oaire.citation.volume | 173 |
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