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Comparing The Effectiveness Of Rank Correlation Statistics

Author

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  • Agostino Tarsitano

    (Dipartimento di Economia e Statistica, Università della Calabria)

Abstract

Rank 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.

Suggested Citation

  • Agostino Tarsitano, 2009. "Comparing The Effectiveness Of Rank Correlation Statistics," Working Papers 200906, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    • Handle: RePEc:clb:wpaper:200906
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    File URL: http://www.ecostat.unical.it/RePEc/WorkingPapers/WP06_2009.pdf
    File Function: First version, 2009-04
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    Cited by:

    1. Claudio M. Rocco & Kash Barker & Jose Moronta, 2022. "Determining the best algorithm to detect community structures in networks: application to power systems," Environment Systems and Decisions, Springer, vol. 42(2), pages 251-264, June.
    2. Auer, Benjamin R. & Schuhmacher, Frank, 2013. "Robust evidence on the similarity of Sharpe ratio and drawdown-based hedge fund performance rankings," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 153-165.
    3. Lee, Paul H. & Yu, Philip L.H., 2012. "Mixtures of weighted distance-based models for ranking data with applications in political studies," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2486-2500.
    4. 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.
    5. Holger Weishaupt & Patrik Johansson & Christopher Engström & Sven Nelander & Sergei Silvestrov & Fredrik J Swartling, 2017. "Loss of Conservation of Graph Centralities in Reverse-engineered Transcriptional Regulatory Networks," Methodology and Computing in Applied Probability, Springer, vol. 19(4), pages 1089-1105, December.
    6. Etumnu, Chinonso & Gray, Allan W., 2020. "A Clustering Approach to Understanding Farmers’ Success Strategies," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 52(3), pages 335-351, August.
    7. Mariangela Sciandra & Antonella Plaia & Vincenza Capursi, 2017. "Classification trees for multivariate ordinal response: an application to Student Evaluation Teaching," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 641-655, March.

    More about this item

    Keywords

    Ordinal Data; Nonlinear Association; Weighted Rank Correlation;
    All these keywords.

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