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Nonparametric Frontier Estimation by Linear Programming

Guillaume Bouchard 1 Stéphane Girard 1 Anatoli B. Juditsky 1 Alexander Nazin 2 
1 IS2 - Statistical Inference for Industry and Health
Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
Abstract : A new method for estimating the frontier of a set of points (or a support, in other words) is proposed. The estimates are defined as kernel functions covering all the points and whose associated support is of smallest surface. They are written as linear combinations of kernel functions applied to the points of the sample. The weights of the linear combination are then computed by solving a linear programming problem. In the general case, the solution of the optimization problem is sparse, that is, only a few coefficients are non zero. The corresponding points play the role of support vectors in the statistical learning theory. The L 1-norm for the error of estimation is shown to be almost surely converging to zero, and the rate of convergence is provided.
Keywords : LEAR
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Submitted on : Monday, December 20, 2010 - 8:42:16 AM
Last modification on : Friday, February 4, 2022 - 3:34:29 AM

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Guillaume Bouchard, Stéphane Girard, Anatoli B. Juditsky, Alexander Nazin. Nonparametric Frontier Estimation by Linear Programming. Automation and Remote Control / Avtomatika i Telemekhanika, MAIK Nauka/Interperiodica, 2004, 65 (1), pp.58--64. ⟨10.1023/B:AURC.0000011690.13220.58⟩. ⟨inria-00548244⟩

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