A USE OF NONPARAMETRIC TESTS FOR DEA-DISCRIMINANT ANALYSIS: A METHODOLOGICAL COMPARISON

Toshiyuki Sueyoshi · Shiuh-Nan Hwang

28 citations0/0 files storedAsia Pacific Journal of Operational Research

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Efficiency Analysis Using DEA Statistical Methods and Applications Financial Distress and Bankruptcy Prediction

Abstract

Discriminant Analysis (DA) is a statistical tool that can predict the group membership of a newly sampled observation. Sueyoshi (European Journal of Operational Research, 115 (1999) 564; 131 (2001) 324; 152 (2004) 45) and Sueyoshi and Kirihara (International Journal of Systems Science, 29 (1998) 1249) 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.

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1999RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA 1946SOME EXAMPLES OF DISCRIMINATION 1999DEA-discriminant analysis in the view of goal programming 2001Extended DEA-Discriminant Analysis 2003Mixed integer programming approach of extended DEA–discriminant analysis 1995Artificial Neural Network Models for Pricing Initial Public Offerings 2001Multicriteria Preference Disaggregation for Classification Problems with an Application to Global Investing Risk*1998Efficiency measurement and strategic classification of Japanese banking institutions

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