Frontier estimation with kernel regression on high order moments

JOURNAL OF MULTIVARIATE ANALYSIS

卷:116

Article

Girard, Stephane¹  Guillou, Armelle^2,3  Stupfler, Gilles⁴ 

[1] INRIA Rhone Alpes & LJK, Team Mistis, F-38334 Montbonnot St Martin, St Ismier, France

[2] Univ Strasbourg, F-67084 Strasbourg, France

[3] CNRS, IRMA, UMR 7501, F-67084 Strasbourg, France

[4] Univ Aix Marseille, CERGAM, F-13628 Aix En Provence 1, France

关键词: Frontier estimation;    Kernel estimation;    Consistency;    Asymptotic normality;   

DOI  :  10.1016/j.jmva.2012.12.001

来源: Elsevier

【 摘 要 】

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 in some finite sample situations. (C) 2012 Elsevier Inc. All rights reserved.

【 授权许可】

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