Fitting finite mixture models to censored data

Abstract

For censored response data the class of generalized linear mixed models is considered, in which random intercepts are assumed to be unobserved discrete random group indicators. A Monte Carlo EM algorithm is established to calcu- late the maximum likelihood estimators for all parameters in this model. Finally, this model is applied on data about partly censored hexachlorobenzene concentrations in pumpkin seeds.