For count data, typically, a Poisson model is assumed. However, it has been observed in
various applications that this model does not fit, because of too few or too many zeros in the …

In many situations count data have a large proportion of zeros and the zero-inflated Poisson
regression (ZIP) model may be appropriate. A simple score test for zero-inflation, comparing …

In several real-life examples one encounters count data where the number of zeros is such
that the usual Poisson distribution does not fit the data. Quite often the number of zeros is …

In certain applications involving count data, it is sometimes found that zeros are observed
with a frequency significantly higher (lower) than predicted by the assumed model …

In this paper a zero adjusted discrete model is developed. Such a situation arises when the
proportion of zeros in the data is higher (lower) than that predicted by the original model …

Count data are easily modelled in GLIM using the Poisson distribution. However, in
modelling such data the counts are often aggregated over one or more factors, or important …

This paper discusses a class of Markov zero-inflated Poisson regression models for a time
series of counts with the presence of excess zero relative to a Poisson distribution, in which …

To model count data with excess zeros and excess ones, in their unpublished manuscript,
Melkersson and Olsson (1999) extended the zero-inflated Poisson distribution to a zero-and …

The Poisson regression model is frequently used to analyze count data. Pseudo R-squared
measures for Poisson regression models have recently been proposed and bias …

Count data often show a higher incidence of zero counts than would be expected if the data
were Poisson distributed. Zero‐inflated Poisson regression models are a useful class of …