Bootstrap bandwidth selection in kernel density estimation from a contaminated sample |
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Authors: | Delaigle A Gijbels I |
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Institution: | (1) Institut de Statistique, Université catholique de Louvain, 20 Voie du Roman Pays, Louvain-la-Neuve, Belgium |
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Abstract: | In this paper we consider kernel estimation of a density when the data are contaminated by random noise. More specifically
we deal with the problem of how to choose the bandwidth parameter in practice. A theoretical optimal bandwidth is defined
as the minimizer of the mean integrated squared error. We propose a bootstrap procedure to estimate this optimal bandwidth,
and show its consistency. These results remain valid for the case of no measurement error, and hence also summarize part of
the theory of bootstrap bandwidth selection in ordinary kernel density estimation. The finite sample performance of the proposed
bootstrap selection procedure is demonstrated with a simulation study. An application to a real data example illustrates the
use of the method.
This research was supported by ‘Projet d’Actions de Recherche Concertées’ (No. 98/03-217) from the Belgian government. Financial
support from the IAP research network nr P5/24 of the Belgian State (Federal Office for Scientific, Technical and Cultural
Affairs) is also gratefully acknowledged. |
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Keywords: | Bandwidth selection bootstrap consistency deconvolution errors-in-variables kernel density estimation |
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