A use of nonparametric tests for data envelopment analysis–discriminant analysis: A methodological comparison

Discriminant Analysis (DA) is a statistical tool that can predict the group membership of a newly sampled observation. Sueyoshi, and Sueyoshi and Kirihara have recently proposed a new type of nonparametric DA approach that provides a set of weights of a linear discriminant function, consequently yielding an evaluation score for the determination of group membership. The nonparametric DA is referred to as ‘Data Envelopment Analysis–Discriminant Analysis (DEA–DA)’, because it maintains its discriminant capabilities by incorporating the nonparametric feature of DEA into DA. In this study, a use of two statistical tests is proposed for DEA–DA and its discriminant capability is compared with DEA from a perspective of financial analysis.