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A Use Of Nonparametric Tests For Dea-Discriminant Analysis: A Methodological Comparison

Author

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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.)

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.

Suggested Citation

  • Toshiyuki Sueyoshi & Shiuh-Nan Hwang, 2004. "A Use Of Nonparametric Tests For Dea-Discriminant Analysis: A Methodological Comparison," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 21(02), pages 179-195.
    • Handle: RePEc:wsi:apjorx:v:21:y:2004:i:02:n:s0217595904000126
    DOI: 10.1142/S0217595904000126
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    Cited by:

    1. Hung-Tso Lin & Tsung-Yu Chou & Yen-Ting Chen & Yin-Chi Huang, 2014. "Profitability analysis using IDEA–DA framework," Annals of Operations Research, Springer, vol. 223(1), pages 291-308, December.

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