It is well known that one or more outlying points in the data may adversely affect the consistency of the quasi-likelihood or the likelihood estimators for the regression effects. Similar to the quasi-likelihood approach, the existing outliers-resistant Mallow's type quasi-likelihood (MQL)...

On top of the generalized estimating equation (GEE) approach, there exist two extended generalized estimating equation (EGEE) approaches where two sets of estimating equations are simultaneously solved for the estimation of the regression and the so-called 'working' correlation parameters. The...

The familial-longitudinal data are collected from a large number of groups or families over a small period of time. This type of data exhibit two-way correlations. First, the responses of the members of a family are likely to be correlated. Second, when the familial responses are repeatedly...

This article proposes an autoregressive model for time series of counts with non-stationary means, variances and covariances as functions of certain time-dependant covariates. For the estimation of the regression, overdispersion and correlation index parameters, a conditional generalized...

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...

Liang and Zeger introduced a class of estimating equations that gives consistent estimates of regression parameters and of their variances in the class of generalized linear models for longitudinal data. When the response variable in such models is subject to overdispersion, the oerdispersion...

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...

An unbalanced mixed linear model with two variance components is considered, one variance component (say [sigma]12 = 0) corresponding to a random effect (treatments) and a second variance component (say [sigma]2 0) corresponding to the experimental errors. Sufficient conditions are obtained...