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An Extension of the Traditional Classi cation Rules: the Case of Non-Random Samples

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Author Info
Anuradha Roy (The University of Texas at San Antonio)
Ricardo Leiva (F.C.E., Universidad Nacional de Cuyo)
Abstract

The paper deals with an heuristic generalization of the traditional classi cation rules by incorporating within sample dependencies. The main motivation behind this generalization is to develop a new classi cation rule when training samples are not random, but, jointly equicorrelated.

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File URL: http://business.utsa.edu/wps/MSS/0057MSS-253-2008.pdf
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File Function: Full text
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Publisher Info
Paper provided by College of Business, University of Texas at San Antonio in its series Working Papers with number 0057.

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Length: 14 pages
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Handle: RePEc:tsa:wpaper:0057

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Related research
Keywords: Classi cation rules; Non-random samples; Jointly equicorrelated training vectors;

Find related papers by JEL classification:
C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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