We present two methods for regression transformation diagnostics in the transform-both-sides model (see Carrol and Ruppert, 1984, 1987; Hinkley and Wang, 1988). One is based on the likelihood displacement proposed by Cook and Weisberg (1982) for assessing the influence of individual cases on the...

Homogeneity of dispersion parameters and zero-inflation parameters is a standard assumption in zero-inflated generalized Poisson regression (ZIGPR) models. However, this assumption may be not appropriate in some situations. This work develops a score test for varying dispersion and/or...

Count data with excess zeros arises in many contexts. Here our concern is to develop a Bayesian analysis for the zero-inflated generalized Poisson (ZIGP) regression model to address this problem. This model provides a useful generalization of zero-inflated Poisson model since the generalized...

Zero-inflated Poisson (ZIP) regression model is a popular approach to the analysis of count data with excess zeros. For correlated count data where the observations are either repeated or clustered outcomes from individual subjects, ZIP mixed regression model may be appropriate. However, ZIP...

In this article, we consider some diagnostics for skew-normal nonlinear regression models with AR(1) errors which provide a useful extension of the normal regression models. The estimation of the parameters in the models is studied based on the EM algorithm. Meanwhile, several score tests are...

A preferred point [alpha]-geometry is introduced in this note, which is a natural extension of the work of Critchley et al. (1993, 1994). Using this geometry, the homogeneous structure and the duality are discussed. We also define a new [alpha]-divergence function and produce a flatness...

For the data from multivariate t distributions, it is very hard to make an influence analysis based on the probability density function since its expression is intractable. In this paper, we present a technique for influence analysis based on the mixture distribution and EM algorithm. In fact,...

Homogeneity of dispersion parameters is a standard assumption in inverse Gaussian regression analysis. However, this assumption is not necessarily appropriate. This paper is devoted to the test for varying dispersion in general inverse Gaussian linear regression models. Based on the modified...