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Anisotropic functional deconvolution with long-memory noise: the case of a multi-parameter fractional Wiener sheet

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  • Rida Benhaddou
  • Qing Liu

Abstract

We look into the minimax results for the anisotropic two-dimensional functional deconvolution model with the two-parameter fractional Gaussian noise. We derive the lower bounds for the $L^p $Lp-risk, $1 \leq p 2, and the corresponding convergence rates do not suffer from the curse of dimensionality.

Suggested Citation

  • Rida Benhaddou & Qing Liu, 2019. "Anisotropic functional deconvolution with long-memory noise: the case of a multi-parameter fractional Wiener sheet," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 31(3), pages 567-595, July.
    • Handle: RePEc:taf:gnstxx:v:31:y:2019:i:3:p:567-595
    DOI: 10.1080/10485252.2019.1604953
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