A General Result on the Mean Integrated Squared Error of the Hard Thresholding Wavelet Estimator under α-Mixing Dependence

We consider the estimation of an unknown function f for weakly dependent data (α-mixing) in a general setting. Our contribution is theoretical: we prove that a hard thresholding wavelet estimator attains a sharp rate of convergence under the mean integrated squared error (MISE) over Besov balls with...

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Main Author: Christophe Chesneau
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2014/403764
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author Christophe Chesneau
author_facet Christophe Chesneau
author_sort Christophe Chesneau
collection DOAJ
description We consider the estimation of an unknown function f for weakly dependent data (α-mixing) in a general setting. Our contribution is theoretical: we prove that a hard thresholding wavelet estimator attains a sharp rate of convergence under the mean integrated squared error (MISE) over Besov balls without imposing too restrictive assumptions on the model. Applications are given for two types of inverse problems: the deconvolution density estimation and the density estimation in a GARCH-type model, both improve existing results in this dependent context. Another application concerns the regression model with random design.
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spelling doaj-art-032bf678aa104bb98573247f5ff36ad12025-02-03T01:23:50ZengWileyJournal of Probability and Statistics1687-952X1687-95382014-01-01201410.1155/2014/403764403764A General Result on the Mean Integrated Squared Error of the Hard Thresholding Wavelet Estimator under α-Mixing DependenceChristophe Chesneau0Laboratoire de Mathéematiques Nicolas Oresme, Université de Caen, BP 5186, 14032 Caen Cedex, FranceWe consider the estimation of an unknown function f for weakly dependent data (α-mixing) in a general setting. Our contribution is theoretical: we prove that a hard thresholding wavelet estimator attains a sharp rate of convergence under the mean integrated squared error (MISE) over Besov balls without imposing too restrictive assumptions on the model. Applications are given for two types of inverse problems: the deconvolution density estimation and the density estimation in a GARCH-type model, both improve existing results in this dependent context. Another application concerns the regression model with random design.http://dx.doi.org/10.1155/2014/403764
spellingShingle Christophe Chesneau
A General Result on the Mean Integrated Squared Error of the Hard Thresholding Wavelet Estimator under α-Mixing Dependence
Journal of Probability and Statistics
title A General Result on the Mean Integrated Squared Error of the Hard Thresholding Wavelet Estimator under α-Mixing Dependence
title_full A General Result on the Mean Integrated Squared Error of the Hard Thresholding Wavelet Estimator under α-Mixing Dependence
title_fullStr A General Result on the Mean Integrated Squared Error of the Hard Thresholding Wavelet Estimator under α-Mixing Dependence
title_full_unstemmed A General Result on the Mean Integrated Squared Error of the Hard Thresholding Wavelet Estimator under α-Mixing Dependence
title_short A General Result on the Mean Integrated Squared Error of the Hard Thresholding Wavelet Estimator under α-Mixing Dependence
title_sort general result on the mean integrated squared error of the hard thresholding wavelet estimator under α mixing dependence
url http://dx.doi.org/10.1155/2014/403764
work_keys_str_mv AT christophechesneau ageneralresultonthemeanintegratedsquarederrorofthehardthresholdingwaveletestimatorunderamixingdependence
AT christophechesneau generalresultonthemeanintegratedsquarederrorofthehardthresholdingwaveletestimatorunderamixingdependence