Title: Bias-correction in DEA efficiency scores using simulated beta samples: an alternative view of bootstrapping in DEA

Authors: Parakramaweera Sunil Dharmapala

Addresses: Department of Operations Management and Business Statistics, College of Economics and Political Science, Sultan Qaboos University, P.O. Box 20, Al Khoud 123, Sultanate of Oman

Abstract: Bootstrapping of DEA efficiency scores came into being under the criticism that DEA input/output data may contain random error, and as a result the efficient frontier may be warped by statistical noise. Since the publication of the seminal paper by Simar and Wilson (1998), several researchers have carried out bootstrapping the DEA frontier, re-computing the efficiency scores after correcting the biases and developing confidence intervals for bias-corrected scores. We view bias-correction in DEA efficiency scores from a different perspective by randomising the efficiency scores that follow underlying beta distributions. In a step-by-step process, using the simulated beta samples, we show how to correct the biases of individual scores, construct confidence intervals for the bias-corrected mean scores and derive some statistical results for the estimators used in the process. Finally, we demonstrate this method by applying it to a set of banks.

Keywords: data envelopment analysis; DEA; assurance regions; AR; order statistics; beta distribution; bias-correction; simulation.

DOI: 10.1504/IJMOR.2018.092104

International Journal of Mathematics in Operational Research, 2018 Vol.12 No.4, pp.438 - 456

Received: 27 Aug 2016
Accepted: 28 Oct 2016
Published online: 04 Jun 2018 *

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