  Quantile regression presents a complete picture of the effects on the
location, scale, and shape of the dependent variable at all points, not just
the mean. We focus on two challenges for citation count analysis by quantile
regression: discontinuity and substantial mass points at lower counts. A
Bayesian hurdle quantile regression model for count data with a substantial
mass point at zero was proposed by King and Song (2019). It uses quantile
regression for modeling the nonzero data and logistic regression for modeling
the probability of zeros versus nonzeros. We show that substantial mass points
for low citation counts will nearly certainly also affect parameter estimation
in the quantile regression part of the model, similar to a mass point at zero.
We update the King and Song model by shifting the hurdle point past the main
mass points. This model delivers more accurate quantile regression for
moderately to highly cited articles, especially at quantiles corresponding to
values just beyond the mass points, and enables estimates of the extent to
which factors influence the chances that an article will be low cited. To
illustrate the potential of this method, it is applied to simulated citation
counts and data from Scopus.
