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Journal article
Deriving Mixture Distributions Through Moment-Generating Functions
Journal of statistical theory and applications, Vol.19(3), pp.383-390
09/2020
Web of Science ID: WOS:000581593300003
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.
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- Title
- Deriving Mixture Distributions Through Moment-Generating Functions
- Publication Details
- Journal of statistical theory and applications, Vol.19(3), pp.383-390
- Resource Type
- Journal article
- Publisher
- Springer Netherlands
- Number of pages
- 8
- Copyright
- © 2020, The Authors
- Identifiers
- WOS:000581593300003; 99381378975806600
- Academic Unit
- Mathematics and Statistics; Hal Marcus College of Science and Engineering
- Language
- English