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