Comparing The Effectiveness Of Rank Correlation Statistics
AbstractRank correlation is a fundamental tool to express dependence in cases in which the data are arranged in order. There are, by contrast, circumstances where the ordinal association is of a nonlinear type. In this paper we investigate the effectiveness of several measures of rank correlation. These measures have been divided into three classes: conventional rank correlations, weighted rank correlations, correlations of scores. Our findings suggest that none is systematically better than the other in all circumstances. However, a simply weighted version of the Kendall rank correlation coefficient provides plausible answers to many special situations where intercategory distances could not be considered on the same basis.
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Bibliographic InfoPaper provided by Università della Calabria, Dipartimento di Economia, Statistica e Finanza (Ex Dipartimento di Economia e Statistica) in its series Working Papers with number 200906.
Length: 24 pages
Date of creation: Apr 2009
Date of revision:
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Postal: Università della Calabria, Dipartimento di Economia, Statistica e Finanza, Ponte Pietro Bucci, Cubo 0/C, I-87036 Arcavacata di Rende, CS, Italy
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Web page: http://www.unical.it/portale/strutture/dipartimenti_240/disesf/
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Ordinal Data; Nonlinear Association; Weighted Rank Correlation;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-05-02 (All new papers)
- NEP-DCM-2009-05-02 (Discrete Choice Models)
- NEP-ECM-2009-05-02 (Econometrics)
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- Lee, Paul H. & Yu, Philip L.H., 2010. "Distance-based tree models for ranking data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1672-1682, June.
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