The Annals of Statistics

Spatially inhomogeneous linear inverse problems with possible singularities

Marianna Pensky

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The objective of the present paper is to introduce the concept of a spatially inhomogeneous linear inverse problem where the degree of ill-posedness of operator $Q$ depends not only on the scale but also on location. In this case, the rates of convergence are determined by the interaction of four parameters, the smoothness and spatial homogeneity of the unknown function $f$ and degrees of ill-posedness and spatial inhomogeneity of operator $Q$.

Estimators obtained in the paper are based either on wavelet–vaguelette decomposition (if the norms of all vaguelettes are finite) or on a hybrid of wavelet–vaguelette decomposition and Galerkin method (if vaguelettes in the neighborhood of the singularity point have infinite norms). The hybrid estimator is a combination of a linear part in the vicinity of the singularity point and the nonlinear block thresholding wavelet estimator elsewhere. To attain adaptivity, an optimal resolution level for the linear, singularity affected, portion of the estimator is obtained using Lepski [Theory Probab. Appl. 35 (1990) 454–466 and 36 (1991) 682–697] method and is used subsequently as the lowest resolution level for the nonlinear wavelet estimator. We show that convergence rates of the hybrid estimator lie within a logarithmic factor of the optimal minimax convergence rates.

The theory presented in the paper is supplemented by examples of deconvolution with a spatially inhomogeneous kernel and deconvolution in the presence of locally extreme noise or extremely inhomogeneous design. The first two problems are examined via a limited simulation study which demonstrates advantages of the hybrid estimator when the degree of spatial inhomogeneity is high. In addition, we apply the technique to recovery of a convolution signal transmitted via amplitude modulation.

Article information

Ann. Statist., Volume 41, Number 5 (2013), 2668-2697.

First available in Project Euclid: 19 November 2013

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62C10: Bayesian problems; characterization of Bayes procedures 65J10: Equations with linear operators (do not use 65Fxx)
Secondary: 62G05: Estimation 62G20: Asymptotic properties

Linear inverse problems inhomogeneous minimax convergence rates singularity


Pensky, Marianna. Spatially inhomogeneous linear inverse problems with possible singularities. Ann. Statist. 41 (2013), no. 5, 2668--2697. doi:10.1214/13-AOS1166.

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