Show simple item record

hal.structure.identifier
dc.contributor.authorNur, Darfiana*
hal.structure.identifier
dc.contributor.authorAllingham, David*
hal.structure.identifier
dc.contributor.authorRousseau, Judith*
hal.structure.identifier
dc.contributor.authorMengersen, Kerrie*
hal.structure.identifier
dc.contributor.authorMcVinish, Ross*
dc.date.accessioned2009-06-26T10:41:17Z
dc.date.available2009-06-26T10:41:17Z
dc.date.issued2009
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/501
dc.language.isoenen
dc.subjectDNA sequence; hidden Markov model; Bayesian model; sensitivity analysis; α-fetoprotein; Markov chain Monte Carlo; importance sampling.en
dc.subject.ddc519en
dc.titleBayesian hidden Markov Model for DNA segmentation : A prior sensitivity analysisen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherQueensland University of Technology;Australie
dc.contributor.editoruniversityotherUniversity of Newcastle;Australie
dc.description.abstractenThe focus of this paper is on the sensitivity to the specification of the prior in a hidden Markov model describing homogeneous segments of DNA sequences. An intron from the chimpanzee α-fetoprotein gene, which plays an im- portant role in embryonic development in mammals is analysed. Three main aims are considered : (i) to assess the sensitivity to prior specification in Bayesian hidden Markov models for DNA sequence segmentation; (ii) to examine the impact of replacing the standard Dirichlet prior with a mixture Dirichlet prior; and (iii) to propose and illus- trate a more comprehensive approach to sensitivity analysis, using importance sampling. It is obtained that (i) the posterior estimates obtained under a Bayesian hidden Markov model are indeed sensitive to the specification of the prior distributions; (ii) compared with the standard Dirichlet prior, the mixture Dirichlet prior is more flexible, less sensitive to the choice of hyperparameters and less constraining in the analysis, thus improving posterior estimates; and (iii) importance sampling was computationally feasible, fast and effective in allowing a richer sensitivity analysis.en
dc.relation.isversionofjnlnameComputational Statistics and Data Analysis
dc.relation.isversionofjnlvol53en
dc.relation.isversionofjnlissue5en
dc.relation.isversionofjnldate2009-03
dc.relation.isversionofjnlpages1873-1882en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.csda.2008.07.007en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00328181/en/en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
hal.author.functionaut
hal.author.functionaut
hal.author.functionaut
hal.author.functionaut
hal.author.functionaut


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record