Generalized Gamma Model Research Articles

A Study of Distributional Shape in Life Testing

Exponential, Weibull, gamma and log-normal models are frequently used in the analysis of failure time data. By extending the generalized gamma model of Stacy [l5]. Prentice [13] showed that the models listed are all embraced by a single parametric family. Likelihood methods were described for discrimination among the special cases and for assessment of each within the more comprehensive framework. Here, such methods are applied to several recent data sets from the industrial and medical literature in order to study distributional shape. New results are given for the accommodation of censoring and regression variables. The appropriateness of Weibull and lognormal models is emphasized.

Inferential Procedures for the Generalized Gamma Distribution

Abstract The maximum likelihood estimators of the parameters of the generalized gamma distribution are shown to have the property that are distributed independently of a and b. Similar properties are also obtained for some Weibull-type statistics. These results are applied in particular to the problem of discriminating between the Weibull model and the generalized gamma model, and several test statistics are considered. In general, the Weibull model is quite flexible and, unless the sample size is quite large, is perhaps a preferable assumption because of the computational complexity and other difficulties encountered with the generalized gamma distribution.