Methodology for non‐parametric deconvolution when the error distribution is unknown

@article{Delaigle2016MethodologyFN,
  title={Methodology for non‐parametric deconvolution when the error distribution is unknown},
  author={Aurore Delaigle and Peter Hall},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  year={2016},
  volume={78}
}
  • A. Delaigle, P. Hall
  • Published 1 January 2016
  • Mathematics
  • Journal of the Royal Statistical Society: Series B (Statistical Methodology)
In the non‐parametric deconvolution problem, to estimate consistently a density or distribution from a sample of data contaminated by additive random noise, it is often assumed that the noise distribution is completely known or that an additional sample of replicated or validation data is available. Methods also have been suggested for estimating the scale of the error distribution, but they require somewhat restrictive smoothness assumptions on the signal distribution, which can be difficult… 

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