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Mining Nonparametric Frontiers

In: Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis

Author

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  • José H. Dulá

    (Virginia Commonwealth University)

Abstract

Data envelopment analysis (DEA) is firmly anchored in efficiency and productivity paradigms. This research claims new application domains for DEA by releasing it from these moorings. The same reasons why efficient entities are of interest in DEA apply to the geometric equivalent in general point sets since they are based on the data’s magnitude limits relative to the other data points. A framework for non-parametric frontier analysis is derived from a new set of first principles. This chapter deals with the extension of data envelopment analysis to the general problem of mining oriented outliers.

Suggested Citation

  • José H. Dulá, 2007. "Mining Nonparametric Frontiers," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 155-170, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-71607-7_9
    DOI: 10.1007/978-0-387-71607-7_9
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