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Consistent Density Deconvolution under Partially Known Error Distribution

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  • Schwarz, Maik
  • Van Bellegem, Sébastien

Abstract

We 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 Info

Paper provided by Institut d'Économie Industrielle (IDEI), Toulouse in its series IDEI Working Papers with number 632.

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Date of creation: 06 Oct 2009
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Handle: RePEc:ide:wpaper:23156

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Keywords: deconvolution; error measurement; density estimation;

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  1. Hall, P. & Simar, L., 2000. "Estimating a Changepoint, Boundary of Frontier in the Presence of Observation Error," Papers 0012, Catholique de Louvain - Institut de statistique.
  2. Johannes, Jan & Van Bellegem, Sébastien & Vanhems, Anne, 2011. "Convergence Rates For Ill-Posed Inverse Problems With An Unknown Operator," Econometric Theory, Cambridge University Press, vol. 27(03), pages 522-545, June.
  3. Neumann, Michael H., 2007. "Deconvolution from panel data with unknown error distribution," Journal of Multivariate Analysis, Elsevier, vol. 98(10), pages 1955-1968, November.
  4. Li, Tong & Vuong, Quang, 1998. "Nonparametric Estimation of the Measurement Error Model Using Multiple Indicators," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 139-165, May.
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