Score tests for zero-inflated Poisson models
Computational Statistics & Data Analysis
A rich family of generalized Poisson regression models with applications
Mathematics and Computers in Simulation - Special issue: Second special issue: Selected papers of the MSSANZ/IMACS 15th biennial conference on modelling and simulation
Testing for two components in a switching regression model
Computational Statistics & Data Analysis
Short communication: Score tests for overdispersion in zero-inflated Poisson mixed models
Computational Statistics & Data Analysis
An extension of an over-dispersion test for count data
Computational Statistics & Data Analysis
Score tests for zero-inflation and overdispersion in two-level count data
Computational Statistics & Data Analysis
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Zero-inflated Poisson (ZIP) regression model is a popular approach to the analysis of count data with excess zeros. For correlated count data where the observations are either repeated or clustered outcomes from individual subjects, ZIP mixed regression model may be appropriate. However, ZIP model may often fail to fit such data either because of over-dispersion or because of under-dispersion in relation to the Poisson distribution. In this paper, we extend the ZIP mixed regression model to zero-inflated generalized Poisson (ZIGP) mixed regression model, where the base-line discrete distribution is generalized Poisson (GP) distribution, which is a natural extension of standard Poisson distribution. Furthermore, the random effects are considered in both zero-inflated and GP components throughout the paper. An EM algorithm for estimating parameters is proposed based on the best linear unbiased prediction-type (BLUP) log-likelihood and the residual maximum likelihood (REML). Meanwhile, several score tests are presented for testing the ZIP mixed regression model against the ZIGP mixed regression model, and for testing the significance of regression coefficients in zero-inflation and generalized Poisson portion. A numerical example is given to illustrate our methodology and the properties of score test statistics are investigated through Monte Carlo simulations.