Density deconvolution based on wavelets with bounded supports
AbstractUnder the assumption that both convolution densities, g and q, have finite degrees of smoothness, we construct a nonlinear wavelet estimator of the unknown density g based on wavelets with bounded supports. We show that this estimator provides local adaptivity to the unknown smoothness of g and, hence, performs better than the estimator based on Meyer-type wavelets if g has irregular behavior.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 56 (2002)
Issue (Month): 3 (February)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Walter, Gilbert G., 1999. "Density estimation in the presence of noise," Statistics & Probability Letters, Elsevier, vol. 41(3), pages 237-246, February.
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