Consistent density deconvolution under partially known error distribution
We 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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Volume (Year): 80 (2010)
Issue (Month): 3-4 (February)
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References listed on IDEAS
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- 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.
- Hall P. & Simar L., 2002.
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- 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.
- Neumann, Michael H., 2007. "Deconvolution from panel data with unknown error distribution," Journal of Multivariate Analysis, Elsevier, vol. 98(10), pages 1955-1968, November.
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