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

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  • Hall, Peter
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    Abstract

    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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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 32 (1990)
    Issue (Month): 2 (February)
    Pages: 177-203

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    Handle: RePEc:eee:jmvana:v:32:y:1990:i:2:p:177-203

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    Related research

    Keywords: bias bootstrap density estimation mean squared error nonparametric regression smoothing parameter;

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