Risk bounds for new M-estimation problems

Abstract : In this paper, we develop new algorithms for parameter estimation in the case of models type Input/Output in order to represent and to characterize a phenomenon Y. From experimental data Y_{1},...,Y_{n} supposed to be i.i.d from Y, we prove risk bounds qualifying the proposed procedures in terms of the number of experimental data n, computing budget m and model complexity. The methods we present are general enough which should cover a wide range of applications.
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Submitted on : Monday, September 12, 2011 - 6:18:34 PM
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  • HAL Id : hal-00537236, version 2

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Nabil Rachdi, Jean-Claude Fort, Thierry Klein. Risk bounds for new M-estimation problems. ESAIM: Probability and Statistics, EDP Sciences, In press. ⟨hal-00537236v2⟩

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