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

Toshiyuki Sueyoshi () and Shiuh-Nan Hwang ()
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Toshiyuki Sueyoshi: Department of Management, New Mexico Institute of Mining and Technology, 801 Leroy Place, Socorro, NM 87801-4796, USA
Shiuh-Nan Hwang: Ming Chuan University, Graduate School of Management, Chung Shan North Road, Section 5, Taipei, Taiwan, R.O.C.

Asia-Pacific Journal of Operational Research (APJOR), 2004, vol. 21, issue 02, 179-195

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.

Keywords: Data envelop analysis; discriminant analysis; corporate disaster; finance (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0217595904000126

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