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Asymptotics for the Hirsch Index

机译:Hirsch指数的渐近性

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The last decade methods for quantifying the research output of individual researchers have become quite popular in academic policy making. The A-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 A-index. The rate of convergence is non-standard: h~(1/2)/(1 + nf(h)), where/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.
机译:在过去的十年中,用于量化单个研究人员的研究成果的方法在学术政策制定中变得非常流行。 A指数(或Hirsch指数)构成了一个有趣的组合书目量/影响指标,最近引起了很多关注。现在它是一个通用指标,例如可以在Web of Science上获得。在本文中,我们建立了经验A指数的渐近正态性。收敛速度是非标准的:h〜(1/2)/(1 + nf(h)),其中/是引文分布的密度,n是研究者的出版物数量。如果引文遵循极值理论中定义的Pareto型或Weibull型分布,我们的一般结果将专门针对可用于实际目的的结果,例如构造置信区间和h-的成对比较指数。对帕累托型情况的仿真研究表明,渐进理论已经很好地适用于中等样本量。

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