Consistent density deconvolution under partially known error distribution
AbstractWe estimate the distribution of a real-valued random variable from contaminated observations. The additive error is supposed to be normally distributed, but with an unknown variance. The distribution is identifiable from the observations if we restrict the class of considered distributions by a simple condition in the time domain. A minimum distance estimator is shown to be consistent imposing only a slightly stronger assumption than the identification condition.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 80 (2010)
Issue (Month): 3-4 (February)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
Other versions of this item:
- Schwarz, Maik & Van Bellegem, Sébastien, 2009. "Consistent Density Deconvolution under Partially Known Error Distribution," IDEI Working Papers 632, Institut d'Économie Industrielle (IDEI), Toulouse.
- Schwarz, Maik & Van Bellegem, Sébastien, 2009. "Consistent Density Deconvolution under Partially Known Error Distribution," TSE Working Papers 09-097, Toulouse School of Economics (TSE).
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