Bernoulli

  • Bernoulli
  • Volume 9, Number 5 (2003), 783-807.

On adaptive inverse estimation of linear functionals in Hilbert scales

Alexander Goldenshluger and Sergei V. Pereverzev

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Abstract

We address the problem of estimating the value of a linear functional $local asymptotic normalitygle f,x \rangle$ from random noisy observations of $y=Ax$ in Hilbert scales. Both the white noise and density observation models are considered. We propose an estimation procedure that adapts to unknown smoothness of $x$, of $f$, and of the noise covariance operator. It is shown that accuracy of this adaptive estimator is worse only by a logarithmic factor than one could achieve in the case of known smoothness. As an illustrative example, the problem of deconvolving a bivariate density with singular support is considered.

Article information

Source
Bernoulli, Volume 9, Number 5 (2003), 783-807.

Dates
First available in Project Euclid: 17 October 2003

Permanent link to this document
https://projecteuclid.org/euclid.bj/1066418878

Digital Object Identifier
doi:10.3150/bj/1066418878

Mathematical Reviews number (MathSciNet)
MR2047686

Zentralblatt MATH identifier
1055.62034

Keywords
adaptive estimation Hilbert scales inverse problems linear functionals minimax risk regularization

Citation

Goldenshluger, Alexander; Pereverzev, Sergei V. On adaptive inverse estimation of linear functionals in Hilbert scales. Bernoulli 9 (2003), no. 5, 783--807. doi:10.3150/bj/1066418878. https://projecteuclid.org/euclid.bj/1066418878


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