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Efficient estimators for functionals of Markov chains with parametric marginals

dc.contributor.author Wefelmeyer, Wolfgang en_US
dc.contributor.author Penev, Spiridon en_US
dc.contributor.author Peng, Hanxiang en_US
dc.contributor.author Schick, Anton en_US
dc.date.accessioned 2021-11-25T13:41:18Z
dc.date.available 2021-11-25T13:41:18Z
dc.date.issued 2004 en_US
dc.description.abstract Suppose we observe a geometrically ergodic Markov chain with a parametric model for the marginal, but no (further) information about the transition distribution. Then the empirical estimator for a linear functional of the joint law of two successive observations is no longer efficient. We construct an improved estimator and show that it is efficient. The construction is similar to a recent one for bivariate models with parametric marginals. The result applies to discretely observed parametric continuous-time processes. en_US
dc.identifier.issn 0167-7152 en_US
dc.identifier.uri http://hdl.handle.net/1959.4/40279
dc.language English
dc.language.iso EN en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/3.0/au/ en_US
dc.source Legacy MARC en_US
dc.title Efficient estimators for functionals of Markov chains with parametric marginals en_US
dc.type Journal Article en
dcterms.accessRights metadata only access
dspace.entity.type Publication en_US
unsw.accessRights.uri http://purl.org/coar/access_right/c_14cb
unsw.identifier.doiPublisher http://dx.doi.org/10.1016/j.spl.2003.10.022 en_US
unsw.relation.faculty Science
unsw.relation.ispartofjournal Statistics & Probability Letters en_US
unsw.relation.ispartofpagefrompageto 335-345 en_US
unsw.relation.ispartofvolume 66 en_US
unsw.relation.originalPublicationAffiliation Wefelmeyer, Wolfgang en_US
unsw.relation.originalPublicationAffiliation Penev, Spiridon, Mathematics & Statistics, Faculty of Science, UNSW en_US
unsw.relation.originalPublicationAffiliation Peng, Hanxiang en_US
unsw.relation.originalPublicationAffiliation Schick, Anton en_US
unsw.relation.school School of Mathematics & Statistics *
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