On Rigorous Specification of ICAR Models.
Intrinsic (or improper) conditional autoregressions, or ICARs, are widely used in spatial statistics, splines, dynamic linear models, and elsewhere. Such models usually have several variance components, including one for errors and at least one for random effects. Likelihood and Bayesian inference d...
| Publicado en: | American Statistician Vol. 66; no. 1; pp. 42 - 50 |
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| Autores principales: | , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Feb2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=76488135&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 76488135 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Feb2012 vid: 66 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 76488135 10.1080/00031305.2012.654746 ppf: 42 ppct: 8 formats: tig: atl: On Rigorous Specification of ICAR Models. aug: au: Lavine, MichaelL. Hodges, JamesS. affil: Department of Mathematics and Statistics, University of Massachusetts, 300 Massachusetts Avenue, Amherst, MA, 01003-9305 Division of Biostatistics, School of Public Health, University of Minnesota, 410 Church Street Southeast, Minneapolis, MN, 55455 su: Autoregression (Statistics) Regression analysis ARCH model (Econometrics) Mathematical statistics Mathematical variables Bayesian analysis sug: subj: Autoregression (Statistics) Regression analysis ARCH model (Econometrics) Mathematical statistics Mathematical variables Bayesian analysis keyword: Conditional autoregression Improper distributions Intrinsic random fields Markov random fields Conditional autoregression Improper distributions Intrinsic random fields Markov random fields ab: Intrinsic (or improper) conditional autoregressions, or ICARs, are widely used in spatial statistics, splines, dynamic linear models, and elsewhere. Such models usually have several variance components, including one for errors and at least one for random effects. Likelihood and Bayesian inference depend on the likelihood function of those variances. But in the absence of constraints or further specifications that are not inherent to ICARs, the likelihood function is arbitrary and thus so are some inferences. We suggest several ways to add constraints or further specifications, but any choice is merely a convention. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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