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  • 标题:Deriving Mixture Distributions Through Moment-Generating Functions
  • 本地全文:下载
  • 作者:Subhash Bagui ; Jia Liu ; Shen Zhang
  • 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
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