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Statistical inference on the h-index with an application to top-scientist performance

Author

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  • Baccini, A.
  • Barabesi, L.
  • Marcheselli, M.
  • Pratelli, L.

Abstract

Despite the huge amount of literature concerning the h-index, few papers have been devoted to its statistical analysis when a probabilistic distribution is assumed for citation counts. The present contribution mainly aims to divulge the inferential techniques recently introduced by Pratelli et al. (2012), by explaining the details for proper point and set estimation of the theoretical h-index. Moreover, some new achievements on simultaneous inference – addressed to produce suitable scholar comparisons – are carried out. Finally, the analysis of the citation dataset for the Nobel Laureates (in the last five years) and for the Fields medallists (from 2002 onward) is considered in order to exemplify the theoretical issues.

Suggested Citation

  • Baccini, A. & Barabesi, L. & Marcheselli, M. & Pratelli, L., 2012. "Statistical inference on the h-index with an application to top-scientist performance," Journal of Informetrics, Elsevier, vol. 6(4), pages 721-728.
    • Handle: RePEc:eee:infome:v:6:y:2012:i:4:p:721-728
    DOI: 10.1016/j.joi.2012.07.009
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    References listed on IDEAS

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    2. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2014. "Are the authors of highly cited articles also the most productive ones?," Journal of Informetrics, Elsevier, vol. 8(1), pages 89-97.
    3. Marco Valente, 2015. "When the Brightest are not the Best," LEM Papers Series 2015/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    4. Cimenler, Oguz & Reeves, Kingsley A. & Skvoretz, John, 2014. "A regression analysis of researchers’ social network metrics on their citation performance in a college of engineering," Journal of Informetrics, Elsevier, vol. 8(3), pages 667-682.

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