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August 2013 A simple bootstrap method for constructing nonparametric confidence bands for functions
Peter Hall, Joel Horowitz
Ann. Statist. 41(4): 1892-1921 (August 2013). DOI: 10.1214/13-AOS1137

Abstract

Standard approaches to constructing nonparametric confidence bands for functions are frustrated by the impact of bias, which generally is not estimated consistently when using the bootstrap and conventionally smoothed function estimators. To overcome this problem it is common practice to either undersmooth, so as to reduce the impact of bias, or oversmooth, and thereby introduce an explicit or implicit bias estimator. However, these approaches, and others based on nonstandard smoothing methods, complicate the process of inference, for example, by requiring the choice of new, unconventional smoothing parameters and, in the case of undersmoothing, producing relatively wide bands. In this paper we suggest a new approach, which exploits to our advantage one of the difficulties that, in the past, has prevented an attractive solution to the problem—the fact that the standard bootstrap bias estimator suffers from relatively high-frequency stochastic error. The high frequency, together with a technique based on quantiles, can be exploited to dampen down the stochastic error term, leading to relatively narrow, simple-to-construct confidence bands.

Citation

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Peter Hall. Joel Horowitz. "A simple bootstrap method for constructing nonparametric confidence bands for functions." Ann. Statist. 41 (4) 1892 - 1921, August 2013. https://doi.org/10.1214/13-AOS1137

Information

Published: August 2013
First available in Project Euclid: 5 September 2013

zbMATH: 1277.62120
MathSciNet: MR3127852
Digital Object Identifier: 10.1214/13-AOS1137

Subjects:
Primary: 62G07, 62G08
Secondary: 62G09

Rights: Copyright © 2013 Institute of Mathematical Statistics

JOURNAL ARTICLE
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Vol.41 • No. 4 • August 2013
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