Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems

We describe a bootstrap method for estimating mean squared error and smoothing parameter in nonparametric problems. The method involves using a resample of smaller size than the original sample. There are many applications, which are illustrated using the special cases of nonparametric density estimation, nonparametric regression, and tail parameter estimation.

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Year of publication:	1990
Authors:	Hall, Peter
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 32.1990, 2, p. 177-203
Publisher:	Elsevier
Keywords:	bias bootstrap density estimation mean squared error nonparametric regression smoothing parameter

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005221688