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Frontier estimation with kernel regression on high order moments

Stephane 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 when the conditional distribution function decreases at a polynomial rate to zero in the neighborhood of the frontier. The estimator is based on a kernel regression on high moments. It is assumed that the order of the moments goes to infinity while the bandwidth of the kernel goes to zero. We give conditions on these two parameters to obtain the asymptotic normality of the estimator. The good performance of the estimator is illustrated on some finite sample situations.
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Contributor : Stephane Girard <>
Submitted on : Friday, July 9, 2010 - 12:07:49 PM
Last modification on : Tuesday, February 9, 2021 - 3:20:20 PM
Long-term archiving on: : Monday, October 11, 2010 - 9:57:59 AM


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  • HAL Id : hal-00499369, version 1


Stephane Girard, Armelle Guillou, Gilles Stupfler. Frontier estimation with kernel regression on high order moments. 2010. ⟨hal-00499369v1⟩



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