Poisson mixed models are used to analyze a wide variety of cluster count data. These models are commonly developed based on the assumption that the random effects have either the log-normal or the gamma distribution. Obtaining consistent as well as efficient estimates for the parameters involved...

It is well known that the likelihood inferences in dynamic mixed models for count data is extremely complicated. In this paper, we, first, develop a generalized method of moments (GMM) approach for the estimation of the parameters of such models. We then consider an alternative generalized...

Clustered count data are usually analysed using Poisson mixed models based on the assumptions of either gamma distributed or log-normal distributed random effects. As it is difficult to anticipate the true mixed model, the researchers tend to make an arbitrary choice between the assumption of...

American Dollar (USD) and Indian Rupee (INR) play an important role in Mauritian economy. It is important to model the pattern of dependence in their co-movement with respect to Mauritian Rupee (MUR), as this may indicate the export-import behavior in Mauritius. However, it is known that...

In this paper, conditional on random family effects, we consider an auto-regression model for repeated count data and their corresponding time-dependent covariates, collected from the members of a large number of independent families. The count responses, in such a set up, unconditionally...