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A Statistical view of Iterative Methods for Linear Inverse Problems

Ana K. Fermin·Carenne Ludena
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Abstract

In this article we study the problem of recovering the unknown solution of a linear ill-posed problem, via iterative regularization methods. We review the problem of projection-regularization from a statistical point of view. A basic purpose of the paper is the consideration of adaptive model selection for determining regularization parameters. This article introduces a new regularized estimator which has the best possible adaptive properties for a wide range of linear functionals. We derive non asymptotic upper bounds for the mean square error of the estimator and give the optimal convergence rates.

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Ana K. Fermin, Carenne Ludena. 2005-04-04. A Statistical view of Iterative Methods for Linear Inverse Problems. https://arxiv.org/abs/math/0504064

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