A note on the ordinal canonical correlation analysis of two sets of ranking scores

Sudhanshu Mishra ()

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper we have proposed a method to conduct the ordinal canonical correlation analysis (OCCA) that yields ordinal canonical variates and the coefficient of correlation between them, which is analogous to (and a generalization of) the rank correlation coefficient of Spearman. The ordinal canonical variates are themselves analogous to the canonical variates obtained by the conventional canonical correlation analysis (CCCA). Our proposed method is suitable to deal with the multivariable ordinal data arrays. Our examples have shown that in finding canonical rank scores and canonical correlation from an ordinal dataset, the CCCA is suboptimal. The OCCA suggested by us outperforms the conventional method. Moreover, our method can take care of any of the five different schemes of rank ordering. It uses the Particle Swarm Optimizer which is one of the recent and prized meta-heuristics for global optimization. The computer program developed by us is fast and accurate. It has worked very well to conduct the OCCA.

Keywords: Ordinal; Canonical correlation; rank order; rankings; scores; standard competition; modified competition; fractional; dense; Repulsive Particle Swarm; global optimization; computer program; FORTRAN (search for similar items in EconPapers)
JEL-codes: C13 C14 C43 C61 C63 C88 (search for similar items in EconPapers)
Date: 2009-01-16
New Economics Papers: this item is included in nep-cmp, nep-dcm and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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