Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2019

Kernel distribution estimation for grouped data

  • Dades identificatives

    Identificador: RP:4692
    Handle: https://hdl.handle.net/20.500.11797/RP4692
    Autors:
    Barreiro-Ures, DanielCao, RicardoFrancisco-Fernández, MarioReyes, Miguel
    Resum:
    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 estimator 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 selectors 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).

  • Altres:

    Autor segons l'article: Barreiro-Ures, Daniel Cao, Ricardo Francisco-Fernández, Mario Reyes, Miguel
    Paraules clau: Bootstrap bandwidth
    Resum: 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 estimator 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 selectors 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).
    Any de publicació de la revista: 2019
    Tipus de publicació: ##rt.metadata.pkp.peerReviewed## info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article