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

Anuradha Roy and Ricardo Leiva
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Anuradha Roy: The University of Texas at San Antonio
Ricardo Leiva: F.C.E., Universidad Nacional de Cuyo

No 57, Working Papers from College of Business, University of Texas at San Antonio

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.

Keywords: Classi cation rules; Non-random samples; Jointly equicorrelated training vectors (search for similar items in EconPapers)
JEL-codes: C30 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2008-07-20
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