Volume 19, Issue 3, September 2020, Pages 383 - 390
Deriving Mixture Distributions Through Moment-Generating Functions
Authors
Subhash Bagui1, *, Jia Liu1, Shen Zhang2
1Department of Mathematics and Statistics, The University of West Florida, Pensacola, FL, USA
2Department of Statistics, The University of Texas at San Antonio, San Antonio, TX, USA
*Corresponding author. Email: sbagui@uwf.edu
Corresponding Author
Subhash Bagui
Received 10 March 2020, Accepted 14 August 2020, Available Online 2 September 2020.
- DOI
- 10.2991/jsta.d.200826.001How to use a DOI?
- Keywords
- Mixture distributions; Moment-generating functions; Characteristic functions; Hierarchical models; Over-dispersed models
- Abstract
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.
- Copyright
- © 2020 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Subhash Bagui AU - Jia Liu AU - Shen Zhang PY - 2020 DA - 2020/09/02 TI - Deriving Mixture Distributions Through Moment-Generating Functions JO - Journal of Statistical Theory and Applications SP - 383 EP - 390 VL - 19 IS - 3 SN - 2214-1766 UR - https://doi.org/10.2991/jsta.d.200826.001 DO - 10.2991/jsta.d.200826.001 ID - Bagui2020 ER -