Choosing the smoothing parameter in a fourier approach to nonparametric deconvolution of a density estimate

J. Barry, P. Diggle

Research output: Contribution to journalArticlepeer-review

Abstract

In this note we derive a weighted non-linear least squares procedure for choosing the smoothing parameter in a Fourier approach to deconvolution of a density estimate. The method has the advantage over a previous procedure in that it is robust to the range of frequencies over which the model is fitted. A simulation study with different parametric forms for the densities in the convolution equation demonstrates that the method can perform well in practice. A truncated form of the estimator generally has a lower mean asymptotic integrated squared error than an alternative, continuously damped form, but the damped method gives better estimates of tail probabilities.

Original languageEnglish (US)
Pages (from-to)223-232
Number of pages10
JournalJournal of Nonparametric Statistics
Volume4
Issue number3
DOIs
StatePublished - Jan 1 1995
Externally publishedYes

Keywords

  • Deconvolution
  • density estimation
  • Fourier transform
  • smoothing

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability

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