A particular parametric model, based on the counting process theory, and aimed at the analysis of recurrent events is explored. The model is built in the context of reliability of repairable systems and is used to analyze failures of water distribution pipes. The proposed model accounts for...

Small area estimators based on a penalized spline regression model approximating a non-linear but smooth relationship between a response and a given covariate are obtained. In each small area, individual curves are fitted using penalized splines with B-spline bases, exploiting the mixed model...

Vector generalized linear models (VGLMs) as implemented in the vgamR package permit multiple parameters to depend (via inverse link functions) on linear predictors. However it is often the case that one wishes different parameters to be related to each other in some way (i.e., to jointly satisfy...

Poisson regression is important in the analysis of longitudinal count data. However, the variance of responses is often much greater than the sample mean in practice, contradicting the Poisson model. To solve this overdispersion problem, negative binomial regression model was introduced by...

Although sample size calculations for testing a parameter in the Poisson regression model have been previously done, very little attention has been given to the effect of the correlation structure of the explanatory covariates on the sample size. A method to calculate the sample size for the...

A novel procedure to test for linearity and unit root in a nonlinear framework is proposed by introducing a new model–the MT-STAR model–which has similar properties of the ESTAR model but reduces the effects of the identification problem and can also account for asymmetry in the adjustment...

For the analysis of longitudinal data with nonignorable and nonmonotone missing responses, a full likelihood method often requires intensive computation, especially when there are many follow-up times. The authors propose and explore a Monte Carlo method, based on importance sampling, for...

The g-and-h distributional family is generated from a relatively simple transformation of the standard normal and can approximate a broad spectrum of distributions. Consequently, it is easy to use in simulation studies and has been applied in multiple areas, including risk management, stock...