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Nonparametric Frontier Estimation from Noisy Data

  • Maik Schwarz
  • Sébastien Van Bellegem
  • Jean-Pierre Florens
Chapter

Abstract

A new nonparametric estimator of production frontiers is defined and studied when the data set of production units is contaminated by measurement error. The measurement error is assumed to be an additive normal random variable on the input variable, but its variance is unknown. The estimator is a modification of the m-frontier, which necessitates the computation of a consistent estimator of the conditional survival function of the input variable given the output variable. In this paper, the identification and the consistency of a new estimator of the survival function is proved in the presence of additive noise with unknown variance. The performance of the estimator is also studied using simulated data.

Keywords

Data Envelopment Analysis Survival Function Production Unit Consistent Estimator Production Frontier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported by the “Agence National de la Recherche” under contract ANR-09-JCJC-0124-01 and by the IAP research network nr P6/03 of the Belgian Government (Belgian Science Policy). Comments from Ingrid Van Keilegom and an anonymous referee were most helpful to improve the final version of the manuscript. The usual disclaimer applies.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maik Schwarz
    • 1
  • Sébastien Van Bellegem
    • 2
    • 3
  • Jean-Pierre Florens
    • 2
  1. 1.Institut de statistique, biostatistique et sciences actuariellesUniversité catholique de LouvainLouvain-la-NeuveBelgium
  2. 2.Toulouse School of Economics (GREMAQ)University of ToulouseToulouseFrance
  3. 3.Center for Operations Research and EconometricsUniversité catholique de LouvainLouvain-la-NeuveBelgium

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