Abstract
We define a new distance measure for ranking data by using a mixture of 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 Spearman’s grade correlation coefficient on a transformation, operated by the copula function, of the rank denoting the leveloftheimportanceassignedbysubjectsunderclassificationtokobjects. Themixturesofcopulaeareaflexiblewaytomodeldifferenttypesofdependencestructuresin thedataandtoconsiderdifferentsituationsintheclassificationprocess. For example, by using mixtures of copulae with lower and upper tail dependence,we emphasize the agreement on extreme ranks, when extreme ranks are considered more important.
Lingua originale | Inglese |
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Titolo della pubblicazione ospite | Cladag 2017 Book of Short Papers |
Pagine | 1-4 |
Numero di pagine | 4 |
Stato di pubblicazione | Pubblicato - 2017 |
Evento | CLADAG 2017 - Milano, Università di Milano Bicocca Durata: 13 set 2017 → 15 set 2017 |
Convegno
Convegno | CLADAG 2017 |
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Città | Milano, Università di Milano Bicocca |
Periodo | 13/9/17 → 15/9/17 |
Keywords
- copula
- hierarchical cluster analysis
- mixture of copulae
- ranking data