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A bootstrap method is developed to estimate the average squared error of a kernel based nonparametric regression estimator for a given bandwidth. This estimated average squared error is then minimised over the bandwidth to produce a regression estimate. Locally adaptive smoothing and simultaneous confidence bands may be obtained from this bootstrap method.
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