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Log-Density Deconvolution by Wavelet Thresholding

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  • Bigot, Jérôme
  • Van Bellegem, Sébastien

Abstract

This paper proposes a new wavelet-based method for deconvolving a density. The estimator combines the ideas of nonlinear wavelet thresholding with periodised Meyer wavelets and estimation by information projection. It is guaranteed to be in the class of density functions, in particular it is positive everywhere by construction. The asymptotic optimality of the estimator is established in terms of rate of convergence of the Kullback-Leibler discrepancy over Besov classes. Finite sample properties is investigated in detail, and show the excellent empirical performance of the estimator, compared with other recently introduced estimators.

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Bibliographic Info

Paper provided by Institut d'Économie Industrielle (IDEI), Toulouse in its series IDEI Working Papers with number 635.

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Date of creation: 11 Feb 2009
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Handle: RePEc:ide:wpaper:23173

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Related research

Keywords: deconvolution; wavelet thresholding; adaptive estimation;

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Cited by:
  1. Johannes, Jan & Van Bellegem, Sébastien & Vanhems, Anne, 2009. "Convergence Rates for III-Posed Inverse Problems with an Unknown Operator," TSE Working Papers 09-030, Toulouse School of Economics (TSE).

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