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dc.contributor.authorRidgway, James*
dc.date.accessioned2017-01-17T14:51:41Z
dc.date.available2017-01-17T14:51:41Z
dc.date.issued2016
dc.identifier.issn0960-3174
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16172
dc.language.isoenen
dc.subjectGHKen
dc.subjectOrthant probabilityen
dc.subjectPFen
dc.subjectSMCen
dc.subject.ddc519en
dc.titleComputation of Gaussian orthant probabilities in high dimensionen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe study the computation of Gaussian orthant probabilities, i.e. the probability that a Gaussian variable falls inside a quadrant. The Geweke–Hajivassiliou–Keane (GHK) algorithm (Geweke, Comput Sci Stat 23:571–578 1991, Keane, Simulation estimation for panel data models with limited dependent variables, 1993, Hajivassiliou, J Econom 72:85–134, 1996, Genz, J Comput Graph Stat 1:141–149, 1992) is currently used for integrals of dimension greater than 10. In this paper, we show that for Markovian covariances GHK can be interpreted as the estimator of the normalizing constant of a state-space model using sequential importance sampling. We show for an AR(1) the variance of the GHK, properly normalized, diverges exponentially fast with the dimension. As an improvement we propose using a particle filter. We then generalize this idea to arbitrary covariance matrices using Sequential Monte Carlo with properly tailored MCMC moves. We show empirically that this can lead to drastic improvements on currently used algorithms. We also extend the framework to orthants of mixture of Gaussians (Student, Cauchy, etc.), and to the simulation of truncated Gaussians.en
dc.relation.isversionofjnlnameStatistics and Computing
dc.relation.isversionofjnlvol26en
dc.relation.isversionofjnlissue4en
dc.relation.isversionofjnldate2016
dc.relation.isversionofjnlpages899-916en
dc.relation.isversionofdoi10.1007/s11222-015-9578-1en
dc.identifier.urlsitehttp://arxiv.org/abs/1411.1314v2en
dc.relation.isversionofjnlpublisherChapman & Hallen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2017-01-03T11:34:39Z
hal.person.labIds60*
hal.identifierhal-01438314*


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