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Kernel distribution estimation for grouped data
dc.contributor.author | Reyes, Miguel | |
dc.contributor.author | Francisco-Fernández, Mario | |
dc.contributor.author | Cao, Ricardo | |
dc.contributor.author | Barreiro-Ures, Daniel | |
dc.date.accessioned | 2023-11-27T16:50:47Z | |
dc.date.available | 2023-11-27T16:50:47Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Reyes, M. [et al.]. Kernel distribution estimation for grouped data. Sort: Statistics and Operations Research Transactions, 2019, Vol. 43, Nº. 2, 2019, p. 259-288 | es_ES |
dc.identifier.issn | 1696-2281 | |
dc.identifier.uri | http://hdl.handle.net/2183/34341 | |
dc.description.abstract | [Abstract]: Interval-grouped data appear when the observations are not obtained in continuous time, but monitored in periodical time instants. In this framework, a nonparametric kernel distribution esti- mator is proposed and studied. The asymptotic bias, variance and mean integrated squared error of the new approach are derived. From the asymptotic mean integrated squared error, a plug-in bandwidth is proposed. Additionally, a bootstrap selector to be used in this context is designed. Through a comprehensive simulation study, the behaviour of the estimator and the bandwidth se- lectors considering different scenarios of data grouping is shown. The performance of the different approaches is also illustrated with a real grouped emergence data set of Avena sterilis (wild oat). | es_ES |
dc.description.sponsorship | The authors thank three anonymous referees and the Editor for numerous useful comments that significantly improved this article. The authors also thank Dr. Fernando Bastida and Dr. José Luis González-Andújar for providing the Avena sterilis L. emergence data employed in Section 6. This research has been supported by MINECO grants MTM2014-52876-R and MTM2017-82724-R, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2016-015 and Centro Singular de Investigación de Galicia ED431G/01), all of them through the ERDF. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2016-015 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Institut d'Estadística de Catalunya | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2014-52876-R/ES/INFERENCIA ESTADISTICA COMPLEJA Y DE ALTA DIMENSION: EN GENOMICA, NEUROCIENCIA, ONCOLOGIA, MATERIALES COMPLEJOS, MALHERBOLOGIA, MEDIO AMBIENTE, ENERGIA Y APLICACIONES INDUSTRI | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION | es_ES |
dc.relation.uri | https://doi.org/10.2436/20.8080.02.88 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Bootstrap bandwidth | es_ES |
dc.subject | Cumulative distribution function estimator | es_ES |
dc.subject | Interval data | es_ES |
dc.subject | Plug-in bandwidth | es_ES |
dc.title | Kernel distribution estimation for grouped data | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | SORT (Statistics and Operations Research Transactions) | es_ES |
UDC.volume | 43 | es_ES |
UDC.issue | 2 | es_ES |
UDC.startPage | 259 | es_ES |
UDC.endPage | 288 | es_ES |
dc.identifier.doi | 10.2436/20.8080.02.88 |
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