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A simple relation between the Leimkuhler curve and the mean residual life

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  • Balakrishnan, N.
  • Sarabia, José María
  • Kolev, Nikolai

Abstract

In this paper, a simple relation between the Leimkuhler curve and the mean residual life is established. The result is illustrated with several models commonly used in informetrics, such as exponential, Pareto and lognormal. Finally, relationships with some other reliability concepts are also presented.

Suggested Citation

  • Balakrishnan, N. & Sarabia, José María & Kolev, Nikolai, 2010. "A simple relation between the Leimkuhler curve and the mean residual life," Journal of Informetrics, Elsevier, vol. 4(4), pages 602-607.
    • Handle: RePEc:eee:infome:v:4:y:2010:i:4:p:602-607
    DOI: 10.1016/j.joi.2010.06.009
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    References listed on IDEAS

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    1. Quentin L. Burrell, 2006. "Measuring concentration within and co-concentration between informetric distributions: An empirical study," Scientometrics, Springer;Akadémiai Kiadó, vol. 68(3), pages 441-456, September.
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    8. Sarabia, José María, 2008. "A general definition of the Leimkuhler curve," Journal of Informetrics, Elsevier, vol. 2(2), pages 156-163.
    9. Egghe, L., 2009. "Mathematical derivation of the impact factor distribution," Journal of Informetrics, Elsevier, vol. 3(4), pages 290-295.
    10. Waltman, Ludo & van Eck, Nees Jan, 2009. "Some comments on Egghe's derivation of the impact factor distribution," Journal of Informetrics, Elsevier, vol. 3(4), pages 363-366.
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    Cited by:

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    2. Unnikrishnan Nair, N. & Vineshkumar, B., 2022. "Modelling informetric data using quantile functions," Journal of Informetrics, Elsevier, vol. 16(2).
    3. Sarabia, José María & Prieto, Faustino & Trueba, Carmen, 2012. "Modeling the probabilistic distribution of the impact factor," Journal of Informetrics, Elsevier, vol. 6(1), pages 66-79.

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