A new distance measure is defined for ranking data by using copula functions. This distance evaluates the dissimilarity between subjects expressing their preferences by rankings in order to segment them by hierarchical cluster analysis. The proposed distance builds upon the Spearmans grade correlation coefficient on a transformation of the ranks denoting the levels of the importance assigned by subjects under classification to k objects. The copula is a flexible way to model different types of dependence structures in the data and to consider different situations in the classification process. For example, by using copulae with lower and upper tail dependence, we emphasize the agreement on extreme ranks, when they are considered more important.

Bonanomi, A., Nai Ruscone, M., Osmetti, S. A., Dissimilarity measure for ranking data via copula, in Ana Colubi, E. K. A. M. D. (ed.), CFE-CMStatistics 2019: 13th International Conference on Computational and Financial Econometrics (CFE 2019) and 12th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2019), Senate House & Birkbeck University of London, UK, 14-16 December 2019: programme and abstracts,, ECOSTA ECONOMETRICS AND STATISTICS, London 2019: 46- 46 [https://hdl.handle.net/10807/222464]

Dissimilarity measure for ranking data via copula

Bonanomi, Andrea;Nai Ruscone, Marta;Osmetti, Silvia Angela
2019

Abstract

A new distance measure is defined for ranking data by using copula functions. This distance evaluates the dissimilarity between subjects expressing their preferences by rankings in order to segment them by hierarchical cluster analysis. The proposed distance builds upon the Spearmans grade correlation coefficient on a transformation of the ranks denoting the levels of the importance assigned by subjects under classification to k objects. The copula is a flexible way to model different types of dependence structures in the data and to consider different situations in the classification process. For example, by using copulae with lower and upper tail dependence, we emphasize the agreement on extreme ranks, when they are considered more important.
Inglese
CFE-CMStatistics 2019: 13th International Conference on Computational and Financial Econometrics (CFE 2019) and 12th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2019), Senate House & Birkbeck University of London, UK, 14-16 December 2019: programme and abstracts,
978-9963-2227-8-0
ECOSTA ECONOMETRICS AND STATISTICS
Bonanomi, A., Nai Ruscone, M., Osmetti, S. A., Dissimilarity measure for ranking data via copula, in Ana Colubi, E. K. A. M. D. (ed.), CFE-CMStatistics 2019: 13th International Conference on Computational and Financial Econometrics (CFE 2019) and 12th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2019), Senate House & Birkbeck University of London, UK, 14-16 December 2019: programme and abstracts,, ECOSTA ECONOMETRICS AND STATISTICS, London 2019: 46- 46 [https://hdl.handle.net/10807/222464]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/222464
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