Bayesian Influence Diagnostics in Radiocarbon Dating

Linear models constitute the primary statistical technique for any experimental science. A major topic in this area is the detection of influential subsets of data, that is, of observations that are influential in terms of their effect on the estimation of parameters in linear regression or of the t...

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Detalles Bibliográficos
Autores: Fernández Ponce, José María, Palacios Rodríguez, Fátima, Rodríguez Griñolo, María del Rosario
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2012
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/162690
Acceso en línea:https://hdl.handle.net/11441/162690
https://doi.org/10.1080/02664763.2012.725531
Access Level:acceso abierto
Palabra clave:Conditional Bias
Influence Analysis
Outliers
Predictive Approach
Radiocarbon Dating
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spelling Bayesian Influence Diagnostics in Radiocarbon DatingFernández Ponce, José MaríaPalacios Rodríguez, FátimaRodríguez Griñolo, María del RosarioConditional BiasInfluence AnalysisOutliersPredictive ApproachRadiocarbon DatingLinear models constitute the primary statistical technique for any experimental science. A major topic in this area is the detection of influential subsets of data, that is, of observations that are influential in terms of their effect on the estimation of parameters in linear regression or of the total population parameters. Numerous studies exist on radiocarbon dating which propose a value consensus and remove possible outliers after the corresponding testing. An influence analysis for the value consensus from a Bayesian perspective is developed in this article.Taylor & FrancisEstadística e Investigación OperativaFQM328. Métodos cuantitativos en evaluación2012info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/162690https://doi.org/10.1080/02664763.2012.725531reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésJournal of Applied Statistics, 40 (1).https://doi.org/10.1080/02664763.2012.725531info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1626902026-06-17T12:51:07Z
dc.title.none.fl_str_mv Bayesian Influence Diagnostics in Radiocarbon Dating
title Bayesian Influence Diagnostics in Radiocarbon Dating
spellingShingle Bayesian Influence Diagnostics in Radiocarbon Dating
Fernández Ponce, José María
Conditional Bias
Influence Analysis
Outliers
Predictive Approach
Radiocarbon Dating
title_short Bayesian Influence Diagnostics in Radiocarbon Dating
title_full Bayesian Influence Diagnostics in Radiocarbon Dating
title_fullStr Bayesian Influence Diagnostics in Radiocarbon Dating
title_full_unstemmed Bayesian Influence Diagnostics in Radiocarbon Dating
title_sort Bayesian Influence Diagnostics in Radiocarbon Dating
dc.creator.none.fl_str_mv Fernández Ponce, José María
Palacios Rodríguez, Fátima
Rodríguez Griñolo, María del Rosario
author Fernández Ponce, José María
author_facet Fernández Ponce, José María
Palacios Rodríguez, Fátima
Rodríguez Griñolo, María del Rosario
author_role author
author2 Palacios Rodríguez, Fátima
Rodríguez Griñolo, María del Rosario
author2_role author
author
dc.contributor.none.fl_str_mv Estadística e Investigación Operativa
FQM328. Métodos cuantitativos en evaluación
dc.subject.none.fl_str_mv Conditional Bias
Influence Analysis
Outliers
Predictive Approach
Radiocarbon Dating
topic Conditional Bias
Influence Analysis
Outliers
Predictive Approach
Radiocarbon Dating
description Linear models constitute the primary statistical technique for any experimental science. A major topic in this area is the detection of influential subsets of data, that is, of observations that are influential in terms of their effect on the estimation of parameters in linear regression or of the total population parameters. Numerous studies exist on radiocarbon dating which propose a value consensus and remove possible outliers after the corresponding testing. An influence analysis for the value consensus from a Bayesian perspective is developed in this article.
publishDate 2012
dc.date.none.fl_str_mv 2012
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/162690
https://doi.org/10.1080/02664763.2012.725531
url https://hdl.handle.net/11441/162690
https://doi.org/10.1080/02664763.2012.725531
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Applied Statistics, 40 (1).
https://doi.org/10.1080/02664763.2012.725531
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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