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A Nonparametric Bootstrap Method for Heteroscedastic Functional Data
dc.contributor.author | Fernández-Casal, Rubén | |
dc.contributor.author | Castillo-Páez, Sergio | |
dc.contributor.author | Flores, Miguel | |
dc.date.accessioned | 2024-04-24T07:46:25Z | |
dc.date.available | 2024-04-24T07:46:25Z | |
dc.date.issued | 2024-03 | |
dc.identifier.issn | 1537-2693 | |
dc.identifier.issn | 1085-7117 | |
dc.identifier.uri | http://hdl.handle.net/2183/36330 | |
dc.description | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature | es_ES |
dc.description | The pre-processed data are supplied with the R package npfda, as the ozone data set. The original data can be downloaded from https://uk-air.defra.gov.uk/data. The code used to apply the proposed methodology to the pollution data and the results generated are included in the supplementary material. | es_ES |
dc.description.abstract | [Absctract]: The objective is to provide a nonparametric bootstrap method for functional data that consists of independent realizations of a continuous one-dimensional process. The process is assumed to be nonstationary, with a functional mean and a functional variance, and dependent. The resampling method is based on nonparametric estimates of the model components. Numerical studies were conducted to check the performance of the proposed procedure, by approximating the bias and the standard error of two estimators. A practical application of the proposed approach to pollution data has also been included. Specifically, it is employed to make inference about the annual trend of ground-level ozone concentration at Yarner Wood monitoring station in the United Kingdom. Supplementary material to this paper is provided online. | es_ES |
dc.description.sponsorship | This work has been supported by grant PID2020-113578RB-I00, funded by MCIN/AEI/10.13039/501100011033. The research of Rubén Fernández-Casal has also been supported by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020/14) and by CITIC that is supported by Xunta de Galicia, convenio de colaboración entre la Consellería de Cultura, Educación, Formación Profesional e Universidades y las universidades gallegas para el refuerzo de los centros de investigación del Sistema Universitario de Galicia (CIGUS). The research of Sergio Castillo Páez has also been supported by the Universidad de las Fuerzas Armadas ESPE, from Ecuador. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2020-14 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00/ES/MÉTODOS ESTADÍSTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORÍA Y APLICACIONES | es_ES |
dc.relation.uri | https://doi.org/10.1007/s13253-023-00561-2 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Functional data analysis | es_ES |
dc.subject | Resampling methods | es_ES |
dc.subject | Local linear estimation | es_ES |
dc.subject | Variogram | es_ES |
dc.title | A Nonparametric Bootstrap Method for Heteroscedastic Functional Data | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Journal of Agricultural, Biological and Environmental Statistics | es_ES |
UDC.volume | 29 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | 169 | es_ES |
UDC.endPage | 184 | es_ES |
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