Gamma Regression. E Y α λ i. It is a generalization of the two-parameter gamma distributionSince many distributions commonly used for parametric models in survival analysis such as the Exponential distribution the Weibull distribution and the Gamma distribution are special cases of the generalized gamma it is.
The generalized gamma distribution is a continuous probability distribution with three parameters. Thus a direct test of the presence of heteroscedasticity can be performed with the parameter estimates of the model. The average claim amount can be modeled as having a gamma distributionusing an inverse link.
Thats not a very meaningful value unless you centered your variables to be be mean zero beforehand.
Gamma regression is in the GLM and so you can get many useful quantities for diagnostic purposes such as deviance residuals leverages Cooks distance and so on. The Zelig object stores fields containing everything needed to rerun the Zelig output and all the. The shape parameter is just a multiplier which is equal to the inverse. It is a generalization of the two-parameter gamma distributionSince many distributions commonly used for parametric models in survival analysis such as the Exponential distribution the Weibull distribution and the Gamma distribution are special cases of the generalized gamma it is.
