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Title: Dissimilarity measure for ranking data via mixture of copulae
Authors: Bonanomi, Andrea
Nai Ruscone, Marta
Osmetti, Silvia Angela
Issue Date: 2019
Publisher: Wiley
Bibliographic citation: Bonanomi Andrea, Nai Ruscone Marta, Osmetti Silvia Angela (2019), Dissimilarity measure for ranking data via mixture of copulae. In: Statistical analysis and data mining, vol. 12, n. 5, October 2019, p. 412-425. ISSN 1932-1864. E-ISSN 1932-1872. DOI 10.1002/sam.11402.
Abstract: We define a new distance measure for ranking data using a mixture of copula functions. Our distance measure evaluates the dissimilarity of subjects' ranking preferences to segment them via hierarchical cluster analysis. The proposed distance measure builds upon Spearman grade correlation coefficient on a copula transformation of rank denoting the level of importance assigned by subjects on the classification of k objects. These mixtures of copulae enable flexible modeling of the different types of dependence structures found in data and the consideration of various circumstances in the classification process. For example, by using mixtures of copulae with lower and upper tail dependence, we can emphasize the agreement on extreme ranks when they are considered important.
Journal/Book: Statistical analysis and data mining (online)
ISSN: 1932-1864
Appears in Collections:Contributo in rivista

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