Kernel distribution estimation for grouped data

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http://hdl.handle.net/2183/34341
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España
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Kernel distribution estimation for grouped dataDate
2019Citation
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
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).
Keywords
Bootstrap bandwidth
Cumulative distribution function estimator
Interval data
Plug-in bandwidth
Cumulative distribution function estimator
Interval data
Plug-in bandwidth
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Rights
Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
1696-2281