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Variable elimination in nested DEA models: a statistical approach

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  • Jirawan Jitthavech

Abstract

In this study, a new definition of a relevant variable in a DEA model is proposed for variable selection. The selection procedure is the conventional iterative backward elimination procedure with multiple statistical comparisons. The multiple tests of null hypothesis are reduced to a simple hypothesis test using either the binomial probability or the McNemar test with Bonferroni correction of significant level. From the results based on two simulation populations of moderately and lowly correlated input variables, the proposed procedure using either one of the suggested statistical tests can identify the relevant variables with high accuracy and eliminate the irrelevant variables effectively. In the dataset from a large scale experiment in the US public school education, the reduced model selected by the proposed procedure is shown to be the better approximation of the full model than the ones selected by the Pastor et al. method.

Suggested Citation

  • Jirawan Jitthavech, 2016. "Variable elimination in nested DEA models: a statistical approach," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 27(3), pages 389-410.
    • Handle: RePEc:ids:ijores:v:27:y:2016:i:3:p:389-410
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    Cited by:

    1. Raul Moragues & Juan Aparicio & Miriam Esteve, 2023. "Ranking the Importance of Variables in a Nonparametric Frontier Analysis Using Unsupervised Machine Learning Techniques," Mathematics, MDPI, vol. 11(11), pages 1-24, June.
    2. Magdalena Rybaczewska-Błażejowska & Aneta Masternak-Janus, 2021. "Assessing and Improving the Eco-Efficiency of Manufacturing: Learning and Challenges from a Polish Case Study," Energies, MDPI, vol. 14(23), pages 1-20, December.
    3. Aneta Masternak‐Janus, 2022. "Measuring the efficiency of materials management based on data envelopment analysis approach: the case of Polish regions," Papers in Regional Science, Wiley Blackwell, vol. 101(3), pages 603-618, June.
    4. Villanueva-Cantillo, Jeyms & Munoz-Marquez, Manuel, 2021. "Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 290(2), pages 657-670.

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