A Bayesian Solution to the Behrens-Fisher problem

dc.centroFacultad de Cienciases_ES
dc.contributor.authorGirón González-Torre, F. Javier
dc.contributor.authorCastillo-Vázquez, Carmen
dc.date.accessioned2022-03-10T13:19:18Z
dc.date.available2022-03-10T13:19:18Z
dc.date.created2021
dc.date.issued2021-07
dc.departamentoAnálisis Matemático, Estadística e Investigación Operativa y Matemática Aplicada
dc.description.abstractA simple solution to the Behrens–Fisher problem based on Bayes factors is presented, and its relation with the Behrens–Fisher distribution is explored. The construction of the Bayes factor is based on a simple hierarchical model, and has a closed form based on the densities of general Behrens–Fisher distributions. Simple asymptotic approximations of the Bayes factor, which are functions of the Kullback–Leibler divergence between normal distributions, are given, and it is also proved to be consistent. Some examples and comparisons are also presented.es_ES
dc.description.sponsorshipOpen access funding provided by Universidad de Málaga/CBUA.es_ES
dc.identifier.citationGirón, F. J., & del Castillo, C. (2021). A Bayesian solution to the Behrens–Fisher problem. Revista de La Real Academia de Ciencias Exactas, Fisicas y Naturales - Serie A: Matematicas, 115(4). https://doi.org/10.1007/s13398-021-01098-0es_ES
dc.identifier.doi10.1007/s13398-021-01098-0
dc.identifier.urihttps://hdl.handle.net/10630/23852
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectModelos matemáticoses_ES
dc.subject.otherBehrens-Fisher problemes_ES
dc.subject.otherBayes factores_ES
dc.subject.otherHierarchical modelses_ES
dc.titleA Bayesian Solution to the Behrens-Fisher problemes_ES
dc.typejournal articlees_ES
dc.type.hasVersionVoRes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication06c948bd-c132-482d-b149-716e136aae57
relation.isAuthorOfPublication.latestForDiscovery06c948bd-c132-482d-b149-716e136aae57

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
2021-RACSAM 2021.pdf
Size:
365.76 KB
Format:
Adobe Portable Document Format
Description:

Collections