Show simple item record

hal.structure.identifier
dc.contributor.authorMartin, Gaël*
hal.structure.identifier
dc.contributor.authorMcCabe, Brendan P.M.*
hal.structure.identifier
dc.contributor.authorFrazier, David T.*
hal.structure.identifier
dc.contributor.authorManeesoonthorn, Worapree*
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorRobert, Christian P.*
dc.date.accessioned2019-03-25T13:42:52Z
dc.date.available2019-03-25T13:42:52Z
dc.date.issued2019
dc.identifier.issn1061-8600
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18565
dc.language.isoenen
dc.subjectAlpha-stable distribution
dc.subjectAsymptotic sufficiency
dc.subjectBayesian consistency
dc.subjectLikelihood-free method
dc.subjectStochastic volatility model
dc.subjectUnscented Kalman filter
dc.subject.ddc621.3en
dc.titleAuxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenA computationally simple approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an intractable likelihood by matching summary statistics for the observed data with statistics computed from data simulated from the true process, based on parameter draws from the prior. Draws that produce a “match” between observed and simulated summaries are retained, and used to estimate the inaccessible posterior. With no reduction to a low-dimensional set ofsufficient statistics being possible in the state space setting, we define the summaries as the maximum of an auxiliary likelihood function, and thereby exploit the asymptotic sufficiency of this estimator for the auxiliary parameter vector. We derive conditions under which this approach—including a computationally efficient version based on the auxiliary score—achieves Bayesian consistency. To reduce the well-documented inaccuracy of ABC in multiparameter settings, we propose the separate treatment of each parameter dimension using an integrated likelihood technique. Three stochastic volatility models for which exact Bayesian inference is either computationally challenging, or infeasible, are used for illustration. We demonstrate that our approach compares favorably against an extensive set of approximate and exact comparators. An empirical illustration completes the article. Supplementary materials for this article are available online.
dc.relation.isversionofjnlnameJournal of Computational and Graphical Statistics
dc.relation.isversionofjnlvol28
dc.relation.isversionofjnlissue3
dc.relation.isversionofjnldate2019
dc.relation.isversionofjnlpages508-522
dc.relation.isversionofdoi10.1080/10618600.2018.1552154
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-01961123
dc.relation.isversionofjnlpublisherTaylor & Francis
dc.subject.ddclabelTraitement du signalen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2019-12-18T14:41:28Z
hal.author.functionaut
hal.author.functionaut
hal.author.functionaut
hal.author.functionaut
hal.author.functionaut


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record