It is well known that unobserved heterogeneity can produce misleading estimates in survival analysis (see for example, Allison, 1995, p.233). The goal of this paper is to show how to estimate and apply a single event timing model with heterogeneity to real data with SAS. We consider three different models: An Exponential-gamma model, when individual-level time-to-event is exponentially distribu...

The Swedish physicist, Waloddi Weibull, is renowned for formulating a family of probability distributions for modelling the variability of nonnegative, quantitative, random phenomena (see reference 3). The distribution was originally designed to model the breaking strength of steel (Weibull was scientific advisor to Bofors, the Swedish steel company) but, since then, Weibull analysis has evolve...

Escherichia coli, as a model microorganism, was treated in phosphate-buffered saline under high hydrostatic pressure between 100 and 300 MPa, and the inactivation dynamics was investigated from the viewpoint of predictive microbiology. Inactivation data were curve fitted by typical predictive models: logistic, Gompertz and Weibull functions. Weibull function described the inactivation curve the...

In assessing the insulation withstand level of the electric apparatus, the step-up test method is used. However, we have still many unknown matters regarding the treatment of the results. In this paper, we assume that the underlying probability distribution of failure time with a constant voltage level follows a Weibull distribution and that an inverse power law relationship between the mean li...

In this paper, a modified exponentiated family of distributions is introduced. The new model was built from continuous parent cumulative distribution function and depends on shape parameter. Its most relevant characteristics have been obtained: the probability density function, quantile moments, stochastic ordering, Poisson mixture with our proposal as mixing distribution, order statistics, tai...

The application of continuous time Weibull models on discrete unemployment duration data may produce bias in the estimated shape of the hazard rate. The bias can be substantial even for weekly duration data, and it is seriously aggravated if the Weibull model is erroneously mixed with a Gamma distribution for unobserved heterogeneity.