Work

Frontier estimation via kernel regression on high power-transformed data

Year: 2011

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1103.5956

Authors: Girard, Stéphane, Jacob, Pierre

Institutions: Institut national de recherche en sciences et technologies du numérique, Institut Montpelliérain Alexander Grothendieck, Université Montpellier 2

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2950580321

Cites: 17

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Related to: 20