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Detecting outliers in DEA after correcting biases in efficiency scores using simulated beta samples

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  • Parakramaweera Sunil Dharmapala

Abstract

We address the issue that data used in DEA are possibly contaminated with statistical noise, and as a result, efficiency scores deviate from actual values due to statistical bias. In such situation, detecting outliers could be misleading. Therefore, we propose a method to correct the biases and compute DEA efficiency scores that follow underlying beta distributions, and we use Thompson et al.'s (1996) DEA model with assurance regions. Then, we demonstrate the bias-correction process of the DEA frontier estimates by constructing confidence intervals for the mean scores and use them to detect outliers.

Suggested Citation

  • Parakramaweera Sunil Dharmapala, 2021. "Detecting outliers in DEA after correcting biases in efficiency scores using simulated beta samples," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 19(3), pages 387-400.
    • Handle: RePEc:ids:ijmore:v:19:y:2021:i:3:p:387-400
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