The objective of this paper is to integrate the gener
alized gamma
(GG) distribution into the information theoretic literature. We study information properties of the
GG distribution and provide an assortment of information measures for the
GG family, which includes the exponential, gamma, Weibull, and gener
alized normal distributions as its subfamilies. The measures include entropy representations of the log-likelihood ratio, AIC, and BIC, discriminating information between
GG and its subfamilies, a minimum discriminating information function, power transformation information, and a maximum entropy index of fit to histogram. We provide the full parametric Bayesian inference for the discrimination information measures. We also provide Bayesian inference for the fit of
GG model to histogram, using a semi-parametric Bayesian procedure, referred to as the maximum entropy Dirichlet (MED). The
GG information measures are computed for duration of unemployment and duration of CEO tenure.