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dc.contributor.authorSchäfer, Christian
dc.contributor.authorChopin, Nicolas
dc.date.accessioned2011-02-07T14:46:31Z
dc.date.available2011-02-07T14:46:31Z
dc.date.issued2013
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/5671
dc.descriptionpreprint : http://hal.archives-ouvertes.fr/hal-00561118/fr/
dc.language.isoenen
dc.subjectSequential Monte Carloen
dc.subjectLinear regressionen
dc.subjectVariable selectionen
dc.subjectAdaptive Monte Carloen
dc.subjectMultivariate binary dataen
dc.subject.ddc519en
dc.subject.classificationjelC15en
dc.subject.classificationjelC11en
dc.titleSequential Monte Carlo on large binary sampling spacesen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherCentre de Recherche en Économie et Statistique (CREST) INSEE – École Nationale de la Statistique et de l'Administration Économique;France
dc.description.abstractenA Monte Carlo algorithm is said to be adaptive if it automatically calibrates its current proposal distribution using past simulations. The choice of the parametric family that defines the set of proposal distributions is critical for a good performance. In this paper, we present such a parametric family for adaptive sampling on high-dimensional binary spaces. A practical motivation for this problem is variable selection in a linear regression context. We want to sample from a Bayesian posterior distribution on the model space using an appropriate version of Sequential Monte Carlo. Raw versions of Sequential Monte Carlo are easily implemented using binary vectors with independent components. For high-dimensional problems, however, these simple proposals do not yield satisfactory results. The key to an efficient adaptive algorithm are binary parametric families which take correlations into account, analogously to the multivariate normal distribution on continuous spaces. We provide a review of models for binary data and make one of them work in the context of Sequential Monte Carlo sampling. Computational studies on real life data with about a hundred covariates suggest that, on difficult instances, our Sequential Monte Carlo approach clearly outperforms standard techniques based on Markov chain exploration by orders of magnitude.en
dc.relation.isversionofjnlnameStatistics and Computing
dc.relation.isversionofjnlvol23
dc.relation.isversionofjnlissue2
dc.relation.isversionofjnldate2013
dc.relation.isversionofjnlpages163-184
dc.relation.isversionofdoihttp://dx.doi.org/10.1007/s11222-011-9299-z
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherSpringer
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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