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High Dimensional Matrix Estimation With Unknown Variance Of The Noise

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  • Olga Klopp

    ()
    (CREST)

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    Abstract

    We propose a new pivotal method for estimating high-dimensional matrices. Assume that we observe a small set of entries or linear combinations of entries of an unknown matrix A0 corrupted by noise. We propose a new method for estimating A0 which does not rely on the knowledge or an estimation of the standard deviation of the noise . Our estimator achieves, up to a logarithmic factor, optimal rates of convergence under the Frobenius risk and, thus, has the same prediction performance as previously proposed estimators which rely on the knowledge of . Our method is based on the solution of a convex optimization problem which makes it computationally attractive

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    Bibliographic Info

    Paper provided by Centre de Recherche en Economie et Statistique in its series Working Papers with number 2012-05.

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    Length: 27
    Date of creation: Feb 2012
    Date of revision:
    Handle: RePEc:crs:wpaper:2012-05

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    Related research

    Keywords: Matrix completion; matrix regression; low rank matrix estimation; recovery of the rank;

    This paper has been announced in the following NEP Reports:

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