On pointwise adaptive nonparametric deconvolution

Research output: Contribution to journalArticlepeer-review


We consider estimating an unknown function f from indirect white noise observations with particular emphasis on the problem of nonparametric deconvolution. Nonparametric estimators that can adapt to unknown smoothness of f are developed. The adaptive estimators are specified under two sets of assumptions on the kernel of the convolution transform. In particular, kernels having Fourier transform with polynomially and exponentially decaying tails are considered. It is shown that the proposed estimates possess, in a sense, the best possible abilities for pointwise adaptation.

Original languageEnglish
Pages (from-to)907-925
Number of pages19
Issue number5
StatePublished - 1999


  • Adaptive estimation
  • Deconvolution
  • Rates of convergence

ASJC Scopus subject areas

  • Statistics and Probability


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