In this paper, we provide a framework for investigation and quantification of impact of object-oriented design choices on the defects in software systems. We report the initial results of an extensive case study, which strongly reinforce earlier, mainly anecdotal, evidence that design aspects related to inheritance and communication between classes can be used as indicators of the most defect-p...

This paper represents the comparison between Negative Binomial Regression model and Generalized Poisson Regression model for over-dispersion count data. For this comparison, we used BDHS 2007 data in where the response variable is the total children ever born which is a count data. When the response variable is count, then Poisson Regression Model as a Generalized Linear Model is widely and pop...

A number of methods have been proposed for dealing with extra-Poisson variation when doing regression analysis of count data. This paper studies negative-binomial regression models and examines efficiency and robustness properties of inference procedures based on them. The methods are compared with quasilikelihood methods. RESUME Plusieurs mkthodes ont ktk propokes en vue de traiter le probltme...

In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative binomial (NB) regression model for counts, and present efficient closed-form Bayesian inference; unlike conventional Poisson models, the proposed approach has two free param...

The Poisson-Gamma model has properties that are very similar to the Poisson model discussed in Appendix C, in which the dependent variable i y is modeled as a Poisson variable with a mean i  where the model error is assumed to follow a Gamma distribution. As it names implies, the Poisson-Gamma is a mixture of two distributions and was first derived by Greenwood and Yule (1920). This mixture di...

Clinicians need to predict the number of involved nodes in breast cancer patients in order to ascertain severity, prognosis, and design subsequent treatment. The distribution of involved nodes often displays over-dispersion-a larger variability than expected. Until now, the negative binomial model has been used to describe this distribution assuming that over-dispersion is only due to unobserve...