Robust nonparametric estimation via wavelet median regression

dc.creatorBrown, Lawrence D.
dc.creatorCai, T. Tony
dc.creatorZhou, Harrison H.
dc.date2008-10-27
dc.date.accessioned2026-07-07T10:13:24Z
dc.date.available2026-07-07T10:13:24Z
dc.descriptionIn this paper we develop a nonparametric regression method that is simultaneously adaptive over a wide range of function classes for the regression function and robust over a large collection of error distributions, including those that are heavy-tailed, and may not even possess variances or means. Our approach is to first use local medians to turn the problem of nonparametric regression with unknown noise distribution into a standard Gaussian regression problem and then apply a wavelet block thresholding procedure to construct an estimator of the regression function. It is shown that the estimator simultaneously attains the optimal rate of convergence over a wide range of the Besov classes, without prior knowledge of the smoothness of the underlying functions or prior knowledge of the error distribution. The estimator also automatically adapts to the local smoothness of the underlying function, and attains the local adaptive minimax rate for estimating functions at a point. A key technical result in our development is a quantile coupling theorem which gives a tight bound for the quantile coupling between the sample medians and a normal variable. This median coupling inequality may be of independent interest.
dc.descriptionPublished in at http://dx.doi.org/10.1214/07-AOS513 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0810.4802
dc.identifierhttp://arxiv.org/abs/0810.4802
dc.identifierAnnals of Statistics 2008, Vol. 36, No. 5, 2055-2084
dc.identifierdoi:10.1214/07-AOS513
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/172516
dc.subjectStatistics Theory
dc.subject62G08 (Primary) 62G20 (Secondary)
dc.titleRobust nonparametric estimation via wavelet median regression
dc.typetext

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