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June, 1990 Data-Driven Bandwidth Choice for Density Estimation Based on Dependent Data
Jeffrey D. Hart, Philippe Vieu
Ann. Statist. 18(2): 873-890 (June, 1990). DOI: 10.1214/aos/1176347630

Abstract

The bandwidth selection problem in kernel density estimation is investigated in situations where the observed data are dependent. The classical leave-out technique is extended, and thereby a class of cross-validated bandwidths is defined. These bandwidths are shown to be asymptotically optimal under a strong mixing condition. The leave-one out, or ordinary, form of cross-validation remains asymptotically optimal under the dependence model considered. However, a simulation study shows that when the data are strongly enough correlated, the ordinary version of cross-validation can be improved upon in finite-sized samples.

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Jeffrey D. Hart. Philippe Vieu. "Data-Driven Bandwidth Choice for Density Estimation Based on Dependent Data." Ann. Statist. 18 (2) 873 - 890, June, 1990. https://doi.org/10.1214/aos/1176347630

Information

Published: June, 1990
First available in Project Euclid: 12 April 2007

zbMATH: 0703.62045
MathSciNet: MR1056341
Digital Object Identifier: 10.1214/aos/1176347630

Subjects:
Primary: 65G05
Secondary: 60G10, 60G35, 62G20, 62M10, 62M99

Rights: Copyright © 1990 Institute of Mathematical Statistics

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Vol.18 • No. 2 • June, 1990
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