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 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 InfoPaper provided by Toulouse School of Economics (TSE) in its series TSE Working Papers with number 09-097.
Date of creation: 06 Oct 2009
Date of revision:
deconvolution; error measurement; density estimation;
Other versions of this item:
- Schwarz, Maik & Van Bellegem, Sébastien, 2010. "Consistent density deconvolution under partially known error distribution," Statistics & Probability Letters, Elsevier, vol. 80(3-4), pages 236-241, February.
- Schwarz, Maik & Van Bellegem, Sébastien, 2009. "Consistent Density Deconvolution under Partially Known Error Distribution," IDEI Working Papers 632, Institut d'Économie Industrielle (IDEI), Toulouse.
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