The power variance function distributions, which include the gamma and compound Poisson (CP) distributions among others, are commonly used in frailty models for family data. In a previous paper, we presented a frailty model constructed by randomizing the scale parameter in a CP distribution. When combined with a parametric baseline hazard, this yields a model with heterogeneity on both the indi...

In many situations individuals with the same values for all observed covariables will have important covariables which cannot be measured or which were not measured during the study. These unknown or unmeasured covariables make the subpopulations, as defined by the observable covariables, heterogeneous. The present study explores the affect of unobservable heterogeneity in the context of Weibul...

Abstract The most commonly used regression model in general insurance pricing is the compound Poisson with gamma claim sizes. There are two different parametrizations for this model: Poisson-gamma parametrization and Tweedie’s parametrization. Insurance industry typically prefers We review both parametrizations, provide new results that help to lower computational costs parameter estimation wit...

Introducing a random effect into the Cox model is a useful tool forbuilding hierarchical families of univariate semiparametric regressionsurvival models. Hougaard [1984] used the Laplace transform to buildfrailty models with explicitly defined survival functions and random ef-fects. The family derived from stable distributions was then extended[Aalen, 1992] to frailty va...

background: cox proportional hazard (cph) model is the most widely used model for survival analysis. when there are unobserved/unmeasured individuals factor, then the results of the cox proportional hazard model may not be reliable. the purpose of this study was to compare the results of cph and frailty models in breast cancer (bc) patients. methods: a historical cohort study was carried out us...