文章基本信息

标题：The Kummer Beta Generalized Gamma Distribution
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作者：Gauss M. Cordeiro ; Rodrigo R. Pescim ; Clarice G.B. Demétrio 等
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2014
卷号：12
期号：4
页码：661-698
出版社：Tingmao Publish Company
摘要：A new extension of the generalized gamma distribution with six-parameter called the Kummer beta generalized gamma distribution is introduced and studied. It contains at least 28 special models such as the beta generalized gamma, beta Weibull, beta exponential, generalized gamma, Weibull and gamma distributions and thus could be a better model for analyzing positive skewed data. The new density function can be expressed as a linear combination of generalized gamma densities. Various mathematical properties of the new distribution including explicit expressions for the ordinary and incomplete moments, generating function, mean deviations, entropy, density function of the order statistics and their moments are derived. The elements of the observed information matrix are provided. We discuss the method of maximum likelihood and a Bayesian approach to fit the model parameters. The superiority of the new model is illustrated by means of three real data sets.
关键词：Bayesian analysis; Generalized gamma distribution; Kummer beta generalized distribution; Lifetime data; Maximum likelihood estimation.