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Saturation spaces for regularization methods in inverse problems

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  • Anne Vanhems
  • Jean-Michel Loubes

Abstract

The aim of this article is to characterize the saturation spaces that appear in inverse problems. Such spaces are defined for a regularization method and the rate of convergence of the estimation part of the inverse problem depends on their definition. Here we prove that it is possible to define these spaces as regularity spaces, independent of the choice of the approximation method. Moreover, this intrinsec definition enables us to provide minimax rate of convergence under such assumptions

Suggested Citation

  • Anne Vanhems & Jean-Michel Loubes, 2004. "Saturation spaces for regularization methods in inverse problems," Econometric Society 2004 North American Summer Meetings 380, Econometric Society.
    • Handle: RePEc:ecm:nasm04:380
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    References listed on IDEAS

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    4. Cohen, Albert & Hoffmann, Marc & Reiß, Markus, 2002. "Adaptive wavelet Galerkin methods for linear inverse problems," SFB 373 Discussion Papers 2002,50, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    5. Vanhems, Anne, 2006. "Nonparametric Study Of Solutions Of Differential Equations," Econometric Theory, Cambridge University Press, vol. 22(1), pages 127-157, February.
    6. Jean–Michel Loubes & Sara Van De Geer, 2002. "Adaptive estimation with soft thresholding penalties," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 56(4), pages 453-478, November.
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    More about this item

    Keywords

    Linear inverse problems; regularization methods; structural econometrics;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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