The zero-inflated Poisson model concerns a random event containing excess zero-count data in unit time.

For long spells of absence, the Poisson model was used but no overdispersion was detected.

Earthquake seismology example: an asymptotic Poisson model of seismic risk for large earthquakes.

It also provides a statistical test for the level of dispersion compared to a Poisson model.

They invented a correlation factor for low scores 0-0, 1-0, 0-1 and 1-1, where the independent Poisson model doesn't hold.

He referred to the model as the multiplicative Poisson model.

Rasch referred to this model as the multiplicative Poisson model.

Poisson models were also constructed with days of absence during the week as the dependent variable.

The Poisson models gave fits similar to those obtained from the logistic models.

The two primary assumptions that the Poisson model makes are [9]: