Read the full-text of this article for free

Title: Variable elimination in nested DEA models: a statistical approach

Authors: Jirawan Jitthavech

Addresses: School of Applied Statistics, National Institute of Development Administration, Bangkok 10240, Thailand

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.

Keywords: data envelopment analysis; nested DEA; relevant variables; 2×2 contingency table; structural zero; binomial probability; McNemar test; Bonferroni correction; variable elimination; variable selection; simulation.

DOI: 10.1504/IJOR.2016.078945

International Journal of Operational Research, 2016 Vol.27 No.3, pp.389 - 410

Received: 14 Aug 2015
Accepted: 19 Sep 2015
Published online: 06 Sep 2016 *