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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Volume (Year): 32 (1990)
Issue (Month): 2 (February)
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