Density deconvolution from repeated measurements without symmetry assumption on the errors

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Density deconvolution from repeated measurements without symmetry assumption on the errors

We consider deconvolution from repeated observations with unknown error distribution. So far, this model has mostly been studied under the additional assumption that the errors are symmetric. We construct an estimator for the non-symmetric error case and study its theoretical properties and practical performance. It is interesting to note that we can improve substantially upon the rates of conv...

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On Deconvolution with Repeated Measurements by Aurore Delaigle,

In a large class of statistical inverse problems it is necessary to suppose that the transformation that is inverted is known. Although, in many applications, it is unrealistic to make this assumption, the problem is often insoluble without it. However, if additional data are available, then it is possible to estimate consistently the unknown error density. Data are seldom available directly on...

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Abstract: In many real applications, the distribution of measurement error could vary with each subject or even with each observation so the errors are heteroscedastic. In this paper, we propose a fast algorithm using a simulation-extrapolation (SIMEX) method to recover the unknown density in the case of heteroscedastic contamination. We show the consistency of the estimator and obtain its asym...

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ژورنال

عنوان ژورنال: Journal of Multivariate Analysis

سال: 2015

ISSN: 0047-259X

DOI: 10.1016/j.jmva.2015.04.004