Information measures for generalized gamma family |
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Authors: | Ali Dadpay Ehsan S Soofi Refik Soyer |
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Institution: | 1. Department of Economics, University of Wisconsin-Milwaukee, P.O. Box 431, Milwaukee, WI 53201, USA;2. Sheldon B. Lubar School of Business and Center for Research on International Economics, University of Wisconsin-Milwaukee, P.O. Box 742, Milwaukee, WI 53201, USA;3. Department of Decision Sciences, George Washington University, Washington, DC 20052, USA |
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Abstract: | The objective of this paper is to integrate the generalized 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 generalized 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. |
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Keywords: | C11 C14 C15 C16 C41 C52 |
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