The Deviance Information Criterion in Comparison of Normal Mixing Models

Autor:	Joanna J. J. Wang, Thomas Fung, Eugene Seneta
Rok vydání:	2014
Předmět:	Statistics and Probability business.industry Model selection Machine learning computer.software_genre Bayesian inference Variance-gamma distribution Deviance information criterion symbols.namesake Software Goodness of fit Statistics symbols Artificial intelligence Statistics Probability and Uncertainty business computer Mixing (physics) Mathematics Gibbs sampling
Zdroj:	International Statistical Review. 82:411-421
ISSN:	0306-7734
DOI:	10.1111/insr.12063
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5c541b7b517a94e05ffe51558915ac3e https://doi.org/10.1111/insr.12063 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.