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Asymptotically best bandwidth selectors in kernel density estimation. (English) Zbl 0787.62043

Summary: This paper gives asymptotically best data based choices of the bandwidth of the kernel density estimator. These bandwidth selectors attain the fastest possible rate of convergence to the desired theoretical optimum and the best possible constant coefficient in the spirit of the usual Fisher information, with the use of only nonnegative kernel estimators at all stages of the selection process.

MSC:

62G07 Density estimation
62G20 Asymptotic properties of nonparametric inference
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References:

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