文章基本信息

标题：The Generalized Marshall-Olkin-Kumaraswamy-G family of distributions
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作者：Subrata Chakraborty ; Laba Handique
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2017
卷号：15
期号：3
页码：391-422
出版社：Tingmao Publish Company
摘要：A family of distribution is proposed by using Kumaraswamy-G (GKw−) distribution as the base line distribution in the generalized Marshall-Olkin (GMO)construction. By expanding the probability density function and the survival function as infinite series the proposed family is seen as infinite mixtures of the GKw−distribution. Series expansions of the density function for order statistics are alsoobtained. Moments, moment generating function, Rényi entropy, quantile function, random sample generation, asymptotes, shapes and stochastic orderings are also investigated. Maximum likelihood estimation, their large sample standard error, confidence intervals and method of moment are presented. Three real life illustrations of comparative data modeling applications with some of the important sub models of the family reveals the superiority of the proposed generalization over its sub models.
关键词：Marshall - Olkin -Kumaraswamy-G family; Generalized Marshall-Olkin family; Exponentiated family; Entropy; AIC and Power Weighted Moments.