The productivity of researchers and the impact of the work they do are a preoccupation of universities, research funding agencies, and sometimes even researchers themselves. The h-index (h) is the most popular of different metrics to measure these activities. This research deals with presenting a practical approach to model the h-index based on the total number of citations (N_C) and the duration from the publishing of the first article (D₁). To determine the effect of every factor (N_C and D₁) on h, we applied a set of simple nonlinear regression. The results indicated that both N_C and D₁ had a significant effect on h ( < 0.001). The determination of coefficient for these equations to estimate the h-index was 93.4% and 39.8%, respectively, which verified that the model based on N_C had a better fit. Then, to record the simultaneous effects of N_C and D₁ on h, multiple nonlinear regression was applied. The results indicated that N_C and D₁ had a significant effect on h ( < 0.001). Also, the determination of coefficient for this equation to estimate h was 93.6%. Finally, to model and estimate the h-index, as a function of N_C and D₁, multiple nonlinear quartile regression was used. The goodness of the fitted model was also assessed.

中文翻译：

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基于引文总数和第一篇文章发表持续时间的H指数建模的统计方法

研究人员的生产力及其所做工作的影响是大学，研究资助机构乃至研究人员本身的关注重点。的ħ -index（ħ）是最流行的不同的指标来衡量这些活动的。这项研究致力于提出一种实用的方法，根据被引总数（N _C）和第一篇文章发表的时间（D ₁），为h指数建模。为了确定每个因子（N _C和D ₁）对h的影响，我们应用了一组简单的非线性回归。结果表明N _C和D ₁对h有显着影响（ <0.001）。这些方程估计h指数的系数分别为93.4％和39.8％，这证明基于N _C的模型具有更好的拟合度。然后，为了记录N _C和D ₁对h的同时影响，应用了多元非线性回归。结果表明N _C和D ₁对h（ <0.001）。而且，用于估计h的该方程式的系数确定为93.6％。最后，为了建模和估计h指数，将其作为N _C和D ₁的函数，使用了多个非线性四分位数回归。还评估了拟合模型的优劣。