Jr. Doyle et Rh. Green, CROSS-EVALUATION IN DEA - IMPROVING DISCRIMINATION AMONG DMUS, INFOR. Information systems and operational research, 33(3), 1995, pp. 205-222
There is a need to distinguish among efficient DMUs in Data Envelopmen
t Analysis (DEA). We introduce cross-evaluation in DEA as a logical ex
tension of the reference set count, an idea which is already well esta
blished in the literature as a way of discriminating among efficient D
MUs. We argue that cross-evaluation is more general, and more powerful
than the reference-set count. Next we describe four variants of cross
-evaluation, each with its own particular meaning; then we describe th
eir implementations as secondary goals to the usual DEA efficiency-max
imising primary goal. We compare the performance of the four variants
on a dozen data sets that have appeared in the DEA literature, paying
particular attention to the effect of the different input-output struc
tures among the data sets. We then illustrate, with one constructed ex
ample and one semi-realistic simulation, that cross-evaluation can giv
e better results (in terms of robustly recovering unobserved 'real' ef
ficiencies) than simple DEA efficiency itself. In the discussion we br
iefly touch on other uses of cross-evaluation that make it a useful ad
dition to the DEA toolkit.