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Log-density Deconvolution by Wavelet Thresholding

Bigot, Jérémie and Bellegem, Sébastien Van Log-density Deconvolution by Wavelet Thresholding. (2009) Scandinavian Journal of Statistics, vol. 36 (n° 4). pp. 749-763. ISSN 1467-9469

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Official URL: http://dx.doi.org/10.1111/j.1467-9469.2009.00653.x

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.

Item Type:Article
Additional Information:Thanks to Wiley editor. The definitive version is available at www3.interscience.wiley.com
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Other partners > Ecole des Hautes Etudes en Sciences Sociales - EHESS (FRANCE)
French research institutions > Institut National de la Recherche Agronomique - INRA (FRANCE)
Université de Toulouse > Institut National des Sciences Appliquées de Toulouse - INSA (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Toulouse School of Economics - TSE (FRANCE)
Laboratory name:
Statistics:download
Deposited By: Jérémie Bigot
Deposited On:19 Mar 2013 16:23

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