Within the wide framework of information production processes, we present a conversion formula that expresses the generalised Lorenz (GL) curve of a size-frequency distribution as a function of the corresponding rank-size distribution using a fully discrete modelling approach. Based on this conversion formula, we introduce a somewhat universal model for the GL curve of the empirical size-frequency distribution. This study's approach to determining the GL curve is indirect, as we obtain our model for the size-frequency framework by modelling the rank-size distribution and not by directly modelling the size distribution or the GL curve itself, as is usually done. Our GL curve model is particularly appealing because it provides a simple and economical description of the distribution that depends on only three quantities: the (i) mean size, (ii) mean rank, and (iii) maximal rank. The model's performance in predicting the shape of the empirical GL curve is illustrated through a case study involving citation analysis.

(2019). How mean rank and mean size may determine the generalised Lorenz curve: With application to citation analysis [journal article - articolo]. In JOURNAL OF INFORMETRICS. Retrieved from http://hdl.handle.net/10446/138905

How mean rank and mean size may determine the generalised Lorenz curve: With application to citation analysis

Bertoli-Barsotti, Lucio;Lando, Tommaso
2019-01-01

Abstract

Within the wide framework of information production processes, we present a conversion formula that expresses the generalised Lorenz (GL) curve of a size-frequency distribution as a function of the corresponding rank-size distribution using a fully discrete modelling approach. Based on this conversion formula, we introduce a somewhat universal model for the GL curve of the empirical size-frequency distribution. This study's approach to determining the GL curve is indirect, as we obtain our model for the size-frequency framework by modelling the rank-size distribution and not by directly modelling the size distribution or the GL curve itself, as is usually done. Our GL curve model is particularly appealing because it provides a simple and economical description of the distribution that depends on only three quantities: the (i) mean size, (ii) mean rank, and (iii) maximal rank. The model's performance in predicting the shape of the empirical GL curve is illustrated through a case study involving citation analysis.
articolo
2019
BERTOLI BARSOTTI, Lucio; Lando, Tommaso
(2019). How mean rank and mean size may determine the generalised Lorenz curve: With application to citation analysis [journal article - articolo]. In JOURNAL OF INFORMETRICS. Retrieved from http://hdl.handle.net/10446/138905
File allegato/i alla scheda:
File Dimensione del file Formato  
1-s2.0-S175115771830470X-main.pdf

accesso aperto

Versione: publisher's version - versione editoriale
Licenza: Creative commons
Dimensione del file 822.97 kB
Formato Adobe PDF
822.97 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/138905
Citazioni
  • Scopus 13
  • ???jsp.display-item.citation.isi??? 11
social impact