Bias-correction in DEA efficiency scores using simulated beta samples: an alternative view of bootstrapping in DEA Online publication date: Mon, 04-Jun-2018
by Parakramaweera Sunil Dharmapala
International Journal of Mathematics in Operational Research (IJMOR), Vol. 12, No. 4, 2018
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mathematics in Operational Research (IJMOR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook