Log‐density Deconvolution by Wavelet Thresholding
Jérémie Bigot and
Sebastien Van Bellegem ()
Scandinavian Journal of Statistics, 2009, vol. 36, issue 4, 749-763
Abstract:
Abstract. This paper proposes a new wavelet‐based method for deconvolving a density. The estimator combines the ideas of non‐linear wavelet thresholding with periodized 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 the rate of convergence of the Kullback–Leibler discrepancy over Besov classes. Finite sample properties are investigated in detail, and show the excellent empirical performance of the estimator, compared with other recently introduced estimators.
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
https://doi.org/10.1111/j.1467-9469.2009.00653.x
Related works:
Working Paper: Log-Density Deconvolution by Wavelet Thresholding (2009) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:36:y:2009:i:4:p:749-763
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0303-6898
Access Statistics for this article
Scandinavian Journal of Statistics is currently edited by ÿrnulf Borgan and Bo Lindqvist
More articles in Scandinavian Journal of Statistics from Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association, Swedish Statistical Association
Bibliographic data for series maintained by Wiley Content Delivery ().