Semiparametric mixture models for multivariate count data, with application
提出一种半参数混合模型处理多元计数数据中的过度离散和相关性，通过EM算法估计，无需对随机系数做参数假设，在真实数据中表现优于参数模型。
The analysis of overdispersed counts has been the focus of a wide range of literature, with the general objective of providing reliable parameter estimates in the presence of heterogeneity or dependence among subjects. In this paper we extend the standard variance component models to the analysis of multivariate counts, defining the dependence among counts through a set of correlated random coefficients. Estimation is carried out by numerical integration through an EM algorithm without parametric assumptions upon the random coefficients distribution. The proposed model is computationally parsimonious and, when applied to a real dataset, seems to produce better results than parametric models. A simulation study has been carried out to investigate the behaviour of the proposed models in a series of empirical situations.