Kernel distribution estimation for grouped data

Identificador:  RP:4692

Handle: https://hdl.handle.net/20.500.11797/RP4692

Autores:  Barreiro-Ures, Daniel; Cao, Ricardo; Francisco-Fernández, Mario; Reyes, Miguel

Resumen:
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).