IDEAS home Printed from https://ideas.repec.org/p/tiu/tiutis/2670b5ef-bb04-4ce8-82b2-722695b18b6a.html
   My bibliography  Save this paper

Asymptotics for the Hirsch Index

Author

Listed:
  • Beirlant, J.
  • Einmahl, J.H.J.

    (Tilburg University, School of Economics and Management)

Abstract

. The last decade methods for quantifying the research output of individual researchers have become quite popular in academic policy making. The h‐index (or Hirsch index) constitutes an interesting combined bibliometric volume/impact indicator that has attracted a lot of attention recently. It is now a common indicator, available for instance on the Web of Science. In this article, we establish the asymptotic normality of the empirical h‐index. The rate of convergence is non‐standard: , where f is the density of the citation distribution and n is the number of publications of a researcher. In case that the citations follow a Pareto‐type respectively a Weibull‐type distribution as defined in extreme value theory, our general result specializes well to results that are useful for practical purposes such as the construction of confidence intervals and pairwise comparisons for the h‐index. A simulation study for the Pareto‐type case shows that the asymptotic theory works well for moderate sample sizes already.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Beirlant, J. & Einmahl, J.H.J., 2007. "Asymptotics for the Hirsch Index," Other publications TiSEM 2670b5ef-bb04-4ce8-82b2-7, Tilburg University, School of Economics and Management.
  • Handle: RePEc:tiu:tiutis:2670b5ef-bb04-4ce8-82b2-722695b18b6a
    as

    Download full text from publisher

    File URL: https://pure.uvt.nl/ws/portalfiles/portal/901148/dp2007-86.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jean Diebolt & Laurent Gardes & Stéphane Girard & Armelle Guillou, 2008. "Bias-reduced estimators of the Weibull tail-coefficient," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(2), pages 311-331, August.
    2. Leo Egghe & Ronald Rousseau, 2006. "An informetric model for the Hirsch-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 121-129, October.
    3. Wolfgang Glänzel, 2006. "On the h-index - A mathematical approach to a new measure of publication activity and citation impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 67(2), pages 315-321, May.
    4. Einmahl, John H. J., 1997. "Poisson and Gaussian approximation of weighted local empirical processes," Stochastic Processes and their Applications, Elsevier, vol. 70(1), pages 31-58, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Krisztina Barcza & András Telcs, 2009. "Paretian publication patterns imply Paretian Hirsch index," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(2), pages 513-519, November.
    2. 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.
    3. John Panaretos & Chrisovaladis Malesios, 2009. "Assessing scientific research performance and impact with single indices," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(3), pages 635-670, December.
    4. Paola Cerchiello & Paolo Giudici, 2013. "H Index: A Statistical Proposal," DEM Working Papers Series 039, University of Pavia, Department of Economics and Management.
    5. Paola Cerchiello & Paolo Giudici, 2014. "Financial big data analysis for the estimation of systemic risks," DEM Working Papers Series 086, University of Pavia, Department of Economics and Management.
    6. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
    7. Paola Cerchiello & Paolo Giudici, 2014. "How to measure the quality of financial tweets," DEM Working Papers Series 069, University of Pavia, Department of Economics and Management.
    8. J. Martin Zyl, 2013. "The generalized Pareto distribution fitted to research outputs of countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1099-1109, March.
    9. Wolfgang Glänzel, 2010. "The role of the h-index and the characteristic scores and scales in testing the tail properties of scientometric distributions," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 697-709, June.
    10. Paola Cerchiello & Paolo Giudici, 2014. "On a statistical h index," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 299-312, May.
    11. Wolfgang Glänzel & Henk F. Moed, 2013. "Opinion paper: thoughts and facts on bibliometric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 381-394, July.
    12. Paola Cerchiello & Paolo Giudici, 2015. "A Bayesian h-index: how to measure research impact," DEM Working Papers Series 102, University of Pavia, Department of Economics and Management.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, Star X. & Rousseau, Ronald & Ye, Fred Y., 2011. "h-Degree as a basic measure in weighted networks," Journal of Informetrics, Elsevier, vol. 5(4), pages 668-677.
    2. John H. J. Einmahl & Fan Yang & Chen Zhou, 2021. "Testing the Multivariate Regular Variation Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 907-919, October.
    3. Deming Lin & Tianhui Gong & Wenbin Liu & Martin Meyer, 2020. "An entropy-based measure for the evolution of h index research," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2283-2298, December.
    4. Claudia Klüppelberg & Gabriel Kuhn & Liang Peng, 2008. "Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 701-718, December.
    5. Kwame Boamah‐Addo & Tomasz J. Kozubowski & Anna K. Panorska, 2023. "A discrete truncated Zipf distribution," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 156-187, May.
    6. Jiancheng Guan & Gangbo Wang, 2010. "A comparative study of research performance in nanotechnology for China’s inventor–authors and their non-inventing peers," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 331-343, August.
    7. Fiorenzo Franceschini & Maurizio Galetto & Domenico Maisano & Luca Mastrogiacomo, 2012. "The success-index: an alternative approach to the h-index for evaluating an individual’s research output," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(3), pages 621-641, September.
    8. Einmahl, J.H.J. & de Haan, L.F.M. & Piterbarg, V.I., 2001. "Nonparametric estimation of the spectral measure of an extreme value distribution," Other publications TiSEM c3485b9b-a0bd-456f-9baa-0, Tilburg University, School of Economics and Management.
    9. M. Ryan Haley, 2016. "A ranking of journals for the aspiring health economist," Applied Economics, Taylor & Francis Journals, vol. 48(18), pages 1710-1718, April.
    10. L. Egghe, 2011. "The single publication H-index of papers in the Hirsch-core of a researcher and the indirect H-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 727-739, December.
    11. Lathabai, Hiran H., 2020. "ψ-index: A new overall productivity index for actors of science and technology," Journal of Informetrics, Elsevier, vol. 14(4).
    12. Einmahl, J.H.J. & Krajina, A. & Segers, J., 2011. "An M-Estimator for Tail Dependence in Arbitrary Dimensions," Discussion Paper 2011-013, Tilburg University, Center for Economic Research.
    13. Maziar Montazerian & Edgar Dutra Zanotto & Hellmut Eckert, 2019. "A new parameter for (normalized) evaluation of H-index: countries as a case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 1065-1078, March.
    14. Gardes, Laurent & Girard, Stéphane, 2016. "On the estimation of the functional Weibull tail-coefficient," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 29-45.
    15. J. Martin Zyl, 2013. "The generalized Pareto distribution fitted to research outputs of countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1099-1109, March.
    16. L. Egghe, 2011. "Mathematical derivation of the scale-dependence of the h-index and other h-type indices," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(2), pages 287-292, May.
    17. Rey-Martí, Andrea & Ribeiro-Soriano, Domingo & Palacios-Marqués, Daniel, 2016. "A bibliometric analysis of social entrepreneurship," Journal of Business Research, Elsevier, vol. 69(5), pages 1651-1655.
    18. Pascal Bador & Thierry Lafouge, 2010. "Comparative analysis between impact factor and h-index for pharmacology and psychiatry journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(1), pages 65-79, July.
    19. Estate Khmaladze & Wolfgang Weil, 2008. "Local empirical processes near boundaries of convex bodies," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 813-842, December.
    20. Bertoli-Barsotti, Lucio & Lando, Tommaso, 2019. "How mean rank and mean size may determine the generalised Lorenz curve: With application to citation analysis," Journal of Informetrics, Elsevier, vol. 13(1), pages 387-396.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tiu:tiutis:2670b5ef-bb04-4ce8-82b2-722695b18b6a. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Richard Broekman (email available below). General contact details of provider: https://www.tilburguniversity.edu/about/schools/economics-and-management/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.