In this paper, we propose a solution to the problem of scoring and ranking partially ordered data, by exploiting the spectral properties of so-called matrices of mutual ranking probabilities, a class of matrices which comprise and convey information on the dominance among statistical units. The procedure is optimal in many respects and overcomes the limitations of other ranking tools, which may fail to deliver acceptable solutions, even in trivial cases. We show the algorithm in action, on real data pertaining to the smartness of a subset of European cities

In questo articolo, viene proposto un algoritmo per l’attribuzione di score a dati parzialmente ordinati e per l’estrazione di ranking, basato sull’analisi delle proprieta spettrali delle cosiddette matrici di ` mutual ranking probability. La procedura e ottimale sotto diversi punti di vista e supera i limiti degli algoritmi attualmente ` disponibili, che possono generare ranking sub-ottimali, anche in casi molto semplici. Infine, si propone una breve applicazione a dati reali, relativi alla “smartness” di alcune citta europee.

Fattore, M., Arcagni, A., Maggino, F. (2019). Optimal scoring of partially ordered data,with an application to the ranking of smart cities. In Smart Statistics for Smart Applications - Book of Short Papers SIS 2019 (pp. 855-860). Pearson.

Optimal scoring of partially ordered data,with an application to the ranking of smart cities

Fattore, M;Arcagni, A;
2019

Abstract

In this paper, we propose a solution to the problem of scoring and ranking partially ordered data, by exploiting the spectral properties of so-called matrices of mutual ranking probabilities, a class of matrices which comprise and convey information on the dominance among statistical units. The procedure is optimal in many respects and overcomes the limitations of other ranking tools, which may fail to deliver acceptable solutions, even in trivial cases. We show the algorithm in action, on real data pertaining to the smartness of a subset of European cities
Capitolo o saggio
Multi-indicator system, Mutual ranking probability, Partially ordered set, Ranking, Scoring, Smart City
English
Smart Statistics for Smart Applications - Book of Short Papers SIS 2019
2019
9788891915108
Pearson
855
860
Fattore, M., Arcagni, A., Maggino, F. (2019). Optimal scoring of partially ordered data,with an application to the ranking of smart cities. In Smart Statistics for Smart Applications - Book of Short Papers SIS 2019 (pp. 855-860). Pearson.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/240118
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