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December, 1987 Asymptotically Optimal Bandwidth Selection for Kernel Density Estimators from Randomly Right-Censored Samples
J. S. Marron, W. J. Padgett
Ann. Statist. 15(4): 1520-1535 (December, 1987). DOI: 10.1214/aos/1176350607

Abstract

This paper makes two important contributions to the theory of bandwidth selection for kernel density estimators under right censorship. First, an asymptotic representation of the integrated squared error into easily understood variance and squared bias components is given. Second, it is shown that if the bandwidth is chosen by the data-based method of least-squares cross-validation, then it is asymptotically optimal in a compelling sense. A by-product of the first part is an interesting comparison of the two most popular kernel estimators.

Citation

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J. S. Marron. W. J. Padgett. "Asymptotically Optimal Bandwidth Selection for Kernel Density Estimators from Randomly Right-Censored Samples." Ann. Statist. 15 (4) 1520 - 1535, December, 1987. https://doi.org/10.1214/aos/1176350607

Information

Published: December, 1987
First available in Project Euclid: 12 April 2007

zbMATH: 0657.62038
MathSciNet: MR913571
Digital Object Identifier: 10.1214/aos/1176350607

Subjects:
Primary: 62G05
Secondary: 62G20

Rights: Copyright © 1987 Institute of Mathematical Statistics

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Vol.15 • No. 4 • December, 1987
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