文章基本信息

标题：Deriving Mixture Distributions Through Moment-Generating Functions
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作者：Subhash Bagui ; Jia Liu ; Shen Zhang 等
期刊名称：Journal of Statistical Theory and Applications (JSTA)
电子版ISSN：1538-7887
出版年度：2020
卷号：19
期号：3
页码：383-390
DOI：10.2991/jsta.d.200826.001
语种：English
出版社：Atlantis Press
摘要：This article aims to make use of moment-generating functions (mgfs) to derive the density of mixture distributions from hierarchical models. When the mgf of a mixture distribution doesn't exist, one can extend the approach to characteristic functions to derive the mixture density. This article uses a result given by E.R. Villa, L.A. Escobar, Am. Stat. 60 (2006), 75–80. The present work complements E.R. Villa, L.A. Escobar, Am. Stat. 60 (2006), 75–80 article with many new examples.
关键词：Mixture distributions; Moment-generating functions; Characteristic functions; Hierarchical models; Over-dispersed models