Bootstrap selection of bandwidth and confidence bands for nonparametric regression

Julian J. Faraway

doi:10.1080/00949659008811292

Bootstrap selection of bandwidth and confidence bands for nonparametric regression

Julian J. Faraway

University of Michigan

Research output: Contribution to journal › Article › peer-review

Abstract

A bootstrap method is developed to estimate the average squared error of a kernel based nonparametric regression estimator for a given bandwidth. This estimated average squared error is then minimised over the bandwidth to produce a regression estimate. Locally adaptive smoothing and simultaneous confidence bands may be obtained from this bootstrap method.

Original language	English
Pages (from-to)	37-44
Number of pages	8
Journal	Journal of Statistical Computation and Simulation
Volume	37
Issue number	1-2
DOIs	https://doi.org/10.1080/00949659008811292
Publication status	Published - 1 Oct 1990

Keywords

bandwith selection
bootstrap
confidence bands
Nonparametric regression

ASJC Scopus subject areas

Statistics and Probability
Modelling and Simulation
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1080/00949659008811292

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KW - confidence bands

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Bootstrap selection of bandwidth and confidence bands for nonparametric regression

Abstract

Keywords

ASJC Scopus subject areas

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