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A simple bootstrap method for constructing nonparametric confidence bands for functions. (English) Zbl 1277.62120

Summary: 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. 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 errors. 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.

MSC:

62G09 Nonparametric statistical resampling methods
62G15 Nonparametric tolerance and confidence regions
62G08 Nonparametric regression and quantile regression
65C60 Computational problems in statistics (MSC2010)

Software:

SemiPar; bootstrap
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References:

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