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.