Relating discriminant analysis and data envelopment analysis to one another

Two techniques that have received a great deal of attention recently are Discriminant Analysis (DA) and Data Envelopment Analysis (DEA). While these are two apparently unrelated methods, they do share some noteworthy commonalities. Both can be described as methods for evaluating performance of a group of similar units or observations using linear programming. In each, a set of factors has been measured for all units and a weighting scheme is sought for these factors to differentiate between the successful and the unsuccessful units. These similarities between DA and DEA raise the question of whether some useful middle ground might exist between the two methods, or whether each method might learn something from the other. Exploring these possibilities along with examining the differences and similarities between DA and DEA is the purpose of this paper. The comparison of the two methods in itself provides insight into both methods and provides the taxonomy from which several alternatives to standard DA and DEA are proposed. Two of these methods are developed and discussed in greater detail and appear to have potential usefulness.