Marginalized mixture models for count data from multiple source populations

Mixture distributions provide flexibility in modeling data collected from populations having unexplained heterogeneity. While interpretations of regression parameters from traditional finite mixture models are specific to unobserved subpopulations or latent classes, investigators are often intereste...

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Detalles Bibliográficos
Autores principales: Benecha, Habtamu K., Neelon, Brian, Divaris, Kimon, Preisser, John S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384970/
https://www.ncbi.nlm.nih.gov/pubmed/28446995
http://dx.doi.org/10.1186/s40488-017-0057-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384970/
https://www.ncbi.nlm.nih.gov/pubmed/28446995
http://dx.doi.org/10.1186/s40488-017-0057-4

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