Interpretable reparameterisations of citation models

This paper aims to find the reasons why some citation models can predict a set of specific bib-liometric indices extremely well. We show why fitting a model that preserves the total sum of a vector can be beneficial in the case of heavy-tailed data that are frequently observed in informet-rics and similar disciplines. Based on this observation, we introduce the reparameterised versions of the discrete generalised beta distribution (DGBD) and power law models that preserve the total sum of elements in a citation vector and, as a byproduct, they enjoy much better predictive power when predicting many bibliometric indices as well as partial cumulative sums. This also results in the underlying model parameters' being easier to fit numerically. Moreover, they are also more interpretable. Namely, just like in our recently-introduced 3DSI (three dimensions of scientific impact) model, we have a clear distinction between the coefficients determining the total pro-ductivity (size), total impact (sum), and those that affect the shape of the resulting theoretical curve.