  When a graphical representation of the cumulative percentage of total
citations to articles, ordered from most cited to least cited, is plotted
against the cumulative percentage of articles, we obtain a Leimkuhler curve. In
this study, we noticed that standard Leimkuhler functions may not be sufficient
to provide accurate fits to various empirical informetrics data. Therefore, we
introduce a new approach to Leimkuhler curves by fitting a known probability
density function to the initial Leimkuhler curve, taking into account the
presence of a heterogeneity factor. As a significant contribution to the
existing literature, we introduce a pair of mixture distributions (called PG
and PIG) to bibliometrics. In addition, we present closed-form expressions for
Leimkuhler curves. {Some measures of citation concentration are examined
empirically for the basic models (based on the Power {and Pareto
distributions}) and the mixed models derived from {these}.} An application to
two sources of informetric data was conducted to see how the mixing models
outperform the standard basic models. The different models were fitted using
non-linear least squares estimation.
