L1-Optimal Nonparametric Frontier Estimation via Linear Programming

Authors:
S. Girard;A. Iouditski;A. V. Nazin
Affiliations:
LMC-IMAG, University Grenoble-I, Grenoble, France;LMC-IMAG, University Grenoble-I, Grenoble, France;Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia
Venue:
Automation and Remote Control
Year:
2005

Citing 6
Cited 2

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Abstract

A frontier estimation method for a set of points on a plane is proposed, being optimal in L 1-norm on a given class of β-Holder boundary functions under β 驴 (0, 1]. The estimator is defined as sufficiently regular linear combination of kernel functions centered in the sample points, which covers all these points and whose associated support is of minimal surface. The linear combination weights are calculated via solution of the related linear programming problem. The L 1-norm of the estimation error is demonstrated to be convergent to zero with probability one, with the optimal rate of convergence.