Data-driven wavelet-Fisz methodology for nonparametric function estimation

dc.creatorFryzlewicz, Piotr
dc.date2007-11-06
dc.date2008-10-02
dc.date.accessioned2026-07-07T10:06:30Z
dc.date.available2026-07-07T10:06:30Z
dc.descriptionWe propose a wavelet-based technique for the nonparametric estimation of functions contaminated with noise whose mean and variance are linked via a possibly unknown variance function. Our method, termed the data-driven wavelet-Fisz technique, consists of estimating the variance function via a Nadaraya-Watson estimator, and then performing a wavelet thresholding procedure which uses the estimated variance function and local means of the data to set the thresholds at a suitable level. We demonstrate the mean-square near-optimality of our wavelet estimator over the usual range of Besov classes. To achieve this, we establish an exponential inequality for the Nadaraya-Watson variance function estimator. We discuss various implementation issues concerning our wavelet estimator, and demonstrate its good practical performance. We also show how it leads to a new wavelet-domain data-driven variance-stabilising transform. Our estimator can be applied to a variety of problems, including the estimation of volatilities, spectral densities and Poisson intensities, as well as to a range of problems in which the distribution of the noise is unknown.
dc.descriptionPublished in at http://dx.doi.org/10.1214/07-EJS139 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0711.0883
dc.identifierhttp://arxiv.org/abs/0711.0883
dc.identifierElectronic Journal of Statistics 2008, Vol. 2, 863-896
dc.identifierdoi:10.1214/07-EJS139
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/170346
dc.subjectStatistics Theory
dc.subject62G08 (Primary) 62G05, 62G20 (Secondary)
dc.titleData-driven wavelet-Fisz methodology for nonparametric function estimation
dc.typetext

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