On the Performance of Dirichlet Prior Mixture of Generalized Linear Mixed Models for Zero Truncated Count Data

Abstract

In this study, the performance of Dirichlet Process Mixture of Generalized Linear Mixed Models (DPMGLMMs) was
\nexamined against some competing models for fitting zero-truncated count data. The Bayesian models such as Monte Carlo Markov
\nChain GLMMs, Bayesian Discrete Weibull and the frequentists models such as Zero truncated Poisson, Zero truncated Binomial and
\nZero truncated Geometric models were compared with the proposed DPMGLMMs model. Simulation and life count data from health
\ndomain was used to compare the performance of DPMGLMM with the Bayesian and frequentist models considered in this study. The
\nresults showed that the DPMGLMM outperformed other models considered for fitting count data that is truncated at zero.