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dc.contributor.authorAntoniadis, Anestisen
dc.contributor.authorBigot, J.en
dc.contributor.authorSapatinas, Theofanisen
dc.creatorAntoniadis, Anestisen
dc.creatorBigot, J.en
dc.creatorSapatinas, Theofanisen
dc.date.accessioned2019-12-02T10:33:35Z
dc.date.available2019-12-02T10:33:35Z
dc.date.issued2001
dc.identifier.issn1548-7660
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/56421
dc.description.abstractWavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-variable objects. We discuss in detail wavelet methods in nonparametric regression, where the data are modelled as observations of a signal contaminated with additive Gaussian noise, and provide an extensive review of the vast literature of wavelet shrinkage and wavelet thresholding estimators developed to denoise such data. These estimators arise from a wide range of classical and empirical Bayes methods treating either individual or blocks of wavelet coefficients. We compare various estimators in an extensive simulation study on a variety of sample sizes, test functions, signal-to-noise ratios and wavelet filters. Because there is no single criterion that can adequately summarise the behaviour of an estimator, we use various criteria to measure performance in finite sample situations. Insight into the performance of these estimators is obtained from graphical outputs and numerical tables. In order to provide some hints of how these estimators should be used to analyse real data sets, a detailed practical step-by-step illustration of a wavelet denoising analysis on electrical consumption is provided. Matlab codes are provided so that all figures and tables in this paper can be reproduced.en
dc.sourceJournal of Statistical Softwareen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-0012780893&partnerID=40&md5=2257573a731522478025738e21e8a740
dc.subjectNonparametric regressionen
dc.subjectEM algorithmen
dc.subjectEmpirical Bayesen
dc.subjectWaveletsen
dc.subjectThresholdingen
dc.subjectMonte Carlo experimentsen
dc.subjectShrinkageen
dc.subjectSmoothing Methodsen
dc.titleWavelet estimators in nonparametric regression: A comparative simulation studyen
dc.typeinfo:eu-repo/semantics/article
dc.description.volume6
dc.description.startingpage1
dc.description.endingpage83
dc.author.facultyΣχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :171</p>en
dc.source.abbreviationJ.Stat.Softwareen
dc.contributor.orcidSapatinas, Theofanis [0000-0002-6126-4654]


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