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The Deviance Information Criterion in Comparison of Normal Mixing Models
Author(s) -
Fung Thomas,
Wang Joanna J.J.,
Seneta Eugene
Publication year - 2014
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12063
Subject(s) - deviance information criterion , deviance (statistics) , bayesian information criterion , goodness of fit , gibbs sampling , model selection , statistics , computer science , mixing (physics) , econometrics , bayesian probability , mathematics , bayesian inference , inference , data mining , artificial intelligence , quantum mechanics , physics
Summary Model selection from several non‐nested models by using the deviance information criterion within Bayesian inference Using Gibbs Sampling (BUGS) software needs to be treated with caution. This is particularly important if one can specify a model in various mixing representations, as for the normal variance‐mean mixing distribution occurring in financial contexts. We propose a procedure to compare goodness of fit of several non‐nested models, which uses BUGS software in part.