Hirsch-type characteristics of the tail of distributions. The generalised h-index |
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| Authors: | Wolfgang Glänzel András Schubert |
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| Institution: | 1. National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences, Beijing 100190, China;2. University of Chinese Academy of Sciences, Beijing, China;3. Peking University, Beijing 100871, China;4. Center for Interdisciplinary Information Science Research, Zhengzhou University, Zhengzhou 450001, China;5. National University of Singapore, Singapore 119077, Singapore;1. Consiglio Nazionale delle Ricerche, Department of Engineering, ICT and Technologies for Energy and Transport, Italy;2. Consiglio Nazionale delle Ricerche, Institute of Informatics and Telematics, Italy;3. The University of New South Wales, School of Computer Science and Engineering, Australia;4. Università di Pisa, Department of Information Engineering, Italy;5. Università degli Studi di Milano, Department of Computer Science, Italy;6. Universidad de Murcia, Department of Information and Communications Engineering, Spain;7. University of Maryland, Baltimore County (UMBC), Department of Computer Science and Electrical Engineering, USA;1. Department of Management, Marketing and Entrepreneurship, University of Canterbury, New Zealand;2. Professor of Marketing, Department of Management and Marketing, University of Melbourne, Australia |
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| Abstract: | In this paper a generalisation of the h-index and g-index is given on the basis of non-negative real-valued functionals defined on subspaces of the vector space generated by the ordered samples. Several Hirsch-type measures are defined and their basic properties are analysed. Empirical properties are illustrated using examples from the micro- and meso-level. Among these measures, the h-index proved the most, the arithmetic and geometric g-indices, the least robust measures. The μ-index and the harmonic g-index provide more balanced results and are still robust enough. |
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