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Frontier estimation with kernel regression on high order moments

Stéphane Girard 1 Armelle Guillou 2 Gilles Stupfler 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
Abstract : We present a new method for estimating the frontier of a multidimensional sample. The estimator is based on a kernel regression on high order moments. It is assumed that the order of the moments goes to infinity while the bandwidth of the kernel goes to zero. The consistency of the estimator is proved under mild conditions on these two parameters. The asymptotic normality is also established when the conditional distribution function decreases at a polynomial rate to zero in the neighborhood of the frontier. The good performance of the estimator is illustrated on some finite sample situations.
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Contributor : Stephane Girard <>
Submitted on : Thursday, January 27, 2011 - 3:11:01 PM
Last modification on : Tuesday, February 9, 2021 - 3:20:20 PM
Long-term archiving on: : Thursday, April 28, 2011 - 3:12:14 AM


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  • HAL Id : hal-00499369, version 2


Stéphane Girard, Armelle Guillou, Gilles Stupfler. Frontier estimation with kernel regression on high order moments. 2011. ⟨hal-00499369v2⟩



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