A note on the ordinal canonical correlation analysis of two sets of ranking scores
AbstractIn 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 12796.
Date of creation: 16 Jan 2009
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Ordinal; Canonical correlation; rank order; rankings; scores; standard competition; modified competition; fractional; dense; Repulsive Particle Swarm; global optimization; computer program; FORTRAN;
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-01-24 (All new papers)
- NEP-CMP-2009-01-24 (Computational Economics)
- NEP-DCM-2009-01-24 (Discrete Choice Models)
- NEP-ECM-2009-01-24 (Econometrics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Korhonen, Pekka & Siljamaki, Aapo, 1998. "Ordinal principal component analysis theory and an application," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 26(4), pages 411-424, February.
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