Marginalized mixture models for count data from multiple source populations

Marginalized mixture models for count data from multiple source populations

Abstract 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...

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Bibliographic Details
Main Authors: Habtamu K. Benecha, Brian Neelon, Kimon Divaris, John S. Preisser
Format: Article
Language:English
Published: SpringerOpen 2017-04-01
Series:Journal of Statistical Distributions and Applications
Subjects:
Dental caries
Excess zeros
Marginal inference
Mixture model
Over-dispersion
Zero-inflation
Online Access:http://link.springer.com/article/10.1186/s40488-017-0057-4

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http://link.springer.com/article/10.1186/s40488-017-0057-4

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