ScholarWorks@중앙대학교

초록

This paper introduces a nonparametric Bayesian latent class model tailored to longitudinal count data with an excess of zeros. By embedding zero-inflation mechanisms and allowing for an unbounded number of mixture components, the proposed approach effectively captures heterogeneous subpopulations while accounting for overdispersion. Specifically, an extended normalised gamma process prior links class membership probabilities to relevant predictors, enabling subjects with similar covariate profiles to form latent classes that capture distinct underlying patterns. In comprehensive simulations, the proposed model demonstrates consistently superior predictive performance and lower misclassification rates compared to competing Poisson, zero-inflated Poisson, and Dirichlet process mixture approaches, underscoring its flexibility and accuracy in modelling latent structures for count data. Empirical validation using longitudinal dental caries data from the Iowa Fluoride Study further confirms that the proposed model outperforms well-known competitors. These findings highlight the importance of integrating flexible mixture modelling with explicit zero-inflation components to address both structural zeros and inherent variability in heterogeneous populations.

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