Population-based reversible jump Markov chain Monte Carlo methods for Bayesian variable selection and evaluation under cost limit restrictions

Fouskakis, D; Draper, D

dc.contributor.author	Fouskakis, D	en
dc.contributor.author	Draper, D	en
dc.date.accessioned	2014-03-01T01:58:02Z
dc.date.available	2014-03-01T01:58:02Z
dc.date.issued	2009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28618
dc.subject	bayesian model comparison	en
dc.subject	bayesian variable selection	en
dc.subject	Case Study	en
dc.subject	Data Collection	en
dc.subject	Health Care	en
dc.subject	Markov Chain Monte Carlo	en
dc.subject	Monte Carlo	en
dc.subject	Optimality Criteria	en
dc.subject	Posterior Probability	en
dc.subject	Prediction Accuracy	en
dc.subject	Probability Model	en
dc.subject	Quality of Care	en
dc.subject	Quality of Health Care	en
dc.subject	Research and Development	en
dc.subject	Search Algorithm	en
dc.subject	Simulated Tempering	en
dc.subject	Variable Selection	en
dc.subject	Reversible Jump Markov Chain Monte Carlo	en
dc.subject	Standard Error	en
dc.title	Population-based reversible jump Markov chain Monte Carlo methods for Bayesian variable selection and evaluation under cost limit restrictions	en
heal.type	journalArticle	en
heal.identifier.primary	10.1111/j.1467-9876.2008.00658.x	en
heal.identifier.secondary	http://dx.doi.org/10.1111/j.1467-9876.2008.00658.x	en
heal.publicationDate	2009	en
heal.abstract	Summary. The measurement and improvement of the quality of health care are important areas of current research and development. A judgement of appropriateness of medical out- comes in hospital quality-of-care studies must depend on an assessment of patient sickness at admission to hospital. Indicators of patient sickness often must be abstracted from medi- cal records, and some variables are more	en
heal.journalName	Journal of The Royal Statistical Society Series C-applied Statistics	en
dc.identifier.doi	10.1111/j.1467-9876.2008.00658.x	en