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Quantile regression presents a complete picture of the effects on thelocation, scale, and shape of the dependent variable at all points, not justthe mean. We focus on two challenges for citation count analysis by quantileregression: discontinuity and substantial mass points at lower counts. ABayesian hurdle quantile regression model for count data with a substantialmass point at zero was proposed by King and Song (2019). It uses quantileregression for modeling the nonzero data and logistic regression for modelingthe probability of zeros versus nonzeros. We show that substantial mass pointsfor low citation counts will nearly certainly also affect parameter estimationin 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 mainmass points. This model delivers more accurate quantile regression formoderately to highly cited articles, especially at quantiles corresponding tovalues just beyond the mass points, and enables estimates of the extent towhich factors influence the chances that an article will be low cited. Toillustrate the potential of this method, it is applied to simulated citationcounts and data from Scopus.

A Bayesian Hurdle Quantile Regression Model for Citation Analysis with Mass Points at Lower Values