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Article: On likelihood estimation for discretely observed Marko jump processes

TitleOn likelihood estimation for discretely observed Marko jump processes
Authors
KeywordsDiscrete observations
Likelihood estimator
Markov jump process
Issue Date2007
PublisherBlackwell Publishing Asia. The Journal's web site is located at http://www.blackwellpublishing.com/journals/ANZS
Citation
Australian And New Zealand Journal Of Statistics, 2007, v. 49 n. 1, p. 93-107 How to Cite?
AbstractThe parameter estimation problem for a Markov jump process sampled at equidistant time points is considered here. Unlike the diffusion case where a closed form of the likelihood function is usually unavailable, here an explicit expansion of the likelihood function of the sampled chain is provided. Under suitable ergodicity conditions on the jump process, the consistency and the asymptotic normality of the likelihood estimator are established as the observation period tends to infinity. Simulation experiments are conducted to demonstrate the computational facility of the method. © 2007 Australian Statistical Publishing Association Inc.
Persistent Identifierhttp://hdl.handle.net/10722/132614
ISSN
2015 Impact Factor: 0.431
2015 SCImago Journal Rankings: 0.275
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorDehay, Den_HK
dc.contributor.authorYao, JFen_HK
dc.date.accessioned2011-03-28T09:27:01Z-
dc.date.available2011-03-28T09:27:01Z-
dc.date.issued2007en_HK
dc.identifier.citationAustralian And New Zealand Journal Of Statistics, 2007, v. 49 n. 1, p. 93-107en_HK
dc.identifier.issn1369-1473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/132614-
dc.description.abstractThe parameter estimation problem for a Markov jump process sampled at equidistant time points is considered here. Unlike the diffusion case where a closed form of the likelihood function is usually unavailable, here an explicit expansion of the likelihood function of the sampled chain is provided. Under suitable ergodicity conditions on the jump process, the consistency and the asymptotic normality of the likelihood estimator are established as the observation period tends to infinity. Simulation experiments are conducted to demonstrate the computational facility of the method. © 2007 Australian Statistical Publishing Association Inc.en_HK
dc.languageengen_US
dc.publisherBlackwell Publishing Asia. The Journal's web site is located at http://www.blackwellpublishing.com/journals/ANZSen_HK
dc.relation.ispartofAustralian and New Zealand Journal of Statisticsen_HK
dc.subjectDiscrete observationsen_HK
dc.subjectLikelihood estimatoren_HK
dc.subjectMarkov jump processen_HK
dc.titleOn likelihood estimation for discretely observed Marko jump processesen_HK
dc.typeArticleen_HK
dc.identifier.emailYao, JF: jeffyao@hku.hken_HK
dc.identifier.authorityYao, JF=rp01473en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1111/j.1467-842X.2006.00466.xen_HK
dc.identifier.scopuseid_2-s2.0-33846688007en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33846688007&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume49en_HK
dc.identifier.issue1en_HK
dc.identifier.spage93en_HK
dc.identifier.epage107en_HK
dc.identifier.eissn1467-842X-
dc.identifier.isiWOS:000243909800008-
dc.publisher.placeAustraliaen_HK
dc.identifier.scopusauthoridDehay, D=6507469530en_HK
dc.identifier.scopusauthoridYao, JF=7403503451en_HK
dc.identifier.citeulike1081565-

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