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Although statistical models fit many citation data sets reasonably well withthe best fitting models being the hooked power law and discretised lognormaldistribution, the fits are rarely close. One possible reason is that theremight be more uncited articles than would be predicted by any model if somearticles are inherently uncitable. Using data from 23 different Scopuscategories, this article tests the assumption that removing a proportion ofuncited articles from a citation dataset allows statistical distributions tohave much closer fits. It also introduces two new models, zero inflateddiscretised lognormal distribution and the zero inflated hooked power lawdistribution and algorithms to fit them. In all 23 cases, the zero inflatedversion of the discretised lognormal distribution was an improvement on thestandard version and in 15 out of 23 cases the zero inflated version of thehooked power law was an improvement on the standard version. Without zeroinflation the discretised lognormal models fit the data better than the hookedpower law distribution 6 out of 23 times and with it, the discretised lognormalmodels fit the data better than the hooked power law distribution 9 out of 23times. Apparently uncitable articles seem to occur due to the presence ofacademic-related magazines in Scopus categories. In conclusion, future citationanalysis and research indicators should take into account uncitable articles,and the best fitting distribution for sets of citation counts from a singlesubject and year is either the zero inflated discretised lognormal or zeroinflated hooked power law.

Are there too many uncited articles? Zero inflated variants of the discretised lognormal and hooked power law distributions