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Measuring catch-up growth in malnourished populations

  • Richard S.J. Tol

    ()

    (Department of Economics, University of Sussex
    Institute for Environmental Studies, Vrije Universiteit, Amsterdam, The Netherlands
    Department of Spatial Economics, Vrije Universiteit, Amsterdam, The Netherlands)

Quantile kernel regression is a flexible way to estimate the percentile of a scholar’s quality stratified by a measurable characteristic, without imposing inappropriate assumption about functional form or population distribution. Quantile kernel regression is here applied to identifying the one-in-a-hundred economist per age cohort according to the Hirsch number.

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File URL: http://www.sussex.ac.uk/economics/documents/wps-60-2013.pdf
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Paper provided by Department of Economics, University of Sussex in its series Working Paper Series with number 6013.

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Date of creation: Apr 2013
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Handle: RePEc:sus:susewp:6013
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  1. Tol, Richard S.J., 2013. "The Matthew effect for cohorts of economists," Journal of Informetrics, Elsevier, vol. 7(2), pages 522-527.
  2. Christian Zimmermann, 2007. "Academic Rankings with RePEc," Working papers 2007-36, University of Connecticut, Department of Economics, revised Mar 2009.
  3. Falk, Michael, 1986. "On the estimation of the quantile density function," Statistics & Probability Letters, Elsevier, vol. 4(2), pages 69-73, March.
  4. Beirlant, Jan & Glänzel, Wolfgang & Carbonez, An & Leemans, Herlinde, 2007. "Scoring research output using statistical quantile plotting," Journal of Informetrics, Elsevier, vol. 1(3), pages 185-192.
  5. van Raan, Anthony F.J. & van Leeuwen, Thed N. & Visser, Martijn S. & van Eck, Nees Jan & Waltman, Ludo, 2010. "Rivals for the crown: Reply to Opthof and Leydesdorff," Journal of Informetrics, Elsevier, vol. 4(3), pages 431-435.
  6. Thomas Krichel & Christian Zimmermann, 2005. "The Economics of Open Bibliographic Data Provision," Working papers 2005-01, University of Connecticut, Department of Economics.
  7. M. Ruth & K. Donaghy & P. Kirshen, 2006. "Introduction," Chapters, in: Regional Climate Change and Variability, chapter 1 Edward Elgar.
  8. 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.
  9. Christian Seiler & Klaus Wohlrabe, 2011. "Ranking Economists on the Basis of Many Indicators: An Alternative Approach Using RePEc Data," CESifo Working Paper Series 3691, CESifo Group Munich.
  10. Herranz, Neus & Ruiz-Castillo, Javier, 2011. "Sub-field normalization in the multiplicative case: Average-based citation indicators," CEPR Discussion Papers 8715, C.E.P.R. Discussion Papers.
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