Hierarchical mixture models for zero-inflated correlated count data |
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Authors: | Xue-dong Chen Hong-xing Shi Xue-ren Wang |
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Affiliation: | 1.School of Science,Huzhou University,Huzhou,China;2.School of Primary Education,Chuxiong Normal University,Chuxiong,China;3.Department of Statistics,Yunnan University,Kunming,China |
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Abstract: | Count data with excess zeros are often encountered in many medical, biomedical and public health applications. In this paper, an extension of zero-inflated Poisson mixed regression models is presented for dealing with multilevel data set, referred as hierarchical mixture zero-inflated Poisson mixed regression models. A stochastic EM algorithm is developed for obtaining the ML estimates of interested parameters and a model comparison is also considered for comparing models with different latent classes through BIC criterion. An application to the analysis of count data from a Shanghai Adolescence Fitness Survey and a simulation study illustrate the usefulness and effectiveness of our methodologies. |
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