Frontier estimation via kernel regression on high power-transformed data
Methodology
2011-03-31 v1 Statistics Theory
Statistics Theory
Abstract
We present a new method for estimating the frontier of a multidimensional sample. The estimator is based on a kernel regression on the power-transformed data. We assume that the exponent of the transformation goes to infinity while the bandwidth of the kernel goes to zero. We give conditions on these two parameters to obtain complete convergence and asymptotic normality. The good performance of the estimator is illustrated on some finite sample situations.
Cite
@article{arxiv.1103.5956,
title = {Frontier estimation via kernel regression on high power-transformed data},
author = {Stéphane Girard and Pierre Jacob},
journal= {arXiv preprint arXiv:1103.5956},
year = {2011}
}