Biometrical Journal · 1998 · 126 citations · 0 references
Natural SciencesBiostatisticsStatistical ModelingStatisticsStochastic ModelingCount Data ModelsInferential StatisticsMaximum Likelihood EstimationNonparametric StatisticsPoisson ProcessesStatistical InferenceBiometric ModelingCount Data AnalysisFinite Mixture Count ModelsPoisson DistributionPoisson Maximum Likelihood EstimationPoisson ModelsZero-inflated DistributionsZero-inflated Models
Count data analysis is common in biometry, yet excess zeros frequently invalidate simple Poisson models. The paper argues that zero‑inflated Poisson models can effectively handle such excess‑zero count data. The authors review existing methods and outline validation strategies, including comparison with a nonparametric Poisson mixture maximum‑likelihood estimator.
Analysis of count data is required in many areas of biometric interest. Often the simple Poisson distribution is not appropriate, since an extra-number of zero counts occur in the count data. Some current approaches for the problem at hand are reviewed. It will be argued that these situations can often be easily modeled using the zero-inflated Poisson distribution. A variety of applications are considered in which this occurs. Possibilities are outlined on how the validity of the zero-inflated Poisson can be validated including a comparison with the nonparametric Poisson mixture maximum likelihood estimator.