Abstract
The fact that many informetric data sets exclude the zero-category—corresponding to the nonproducers being unobserved—has led to difficulties in the implementation of Sichel's generalized inverse Gaussian-Poisson (GIGP) process for informetric modeling, despite its theoretical attraction. These computational problems have been surmounted by the development of a program giving maximum likelihood estimates of the parameters of the zero-truncated GIGP. This allows a unified and theoretically sound approach to the fitting of the GIGP and is illustrated using several of the classic informetric data sets. The method also highlights situations in which the model motivating the GIGP is inappropriate. © 1993 John Wiley & Sons, Inc.
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