Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/113819
Citations
Scopus Web of Science® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhu, D.-
dc.contributor.authorChing, W.-
dc.contributor.authorElliott, R.-
dc.contributor.authorSiu, T.-
dc.contributor.authorZhang, L.-
dc.date.issued2017-
dc.identifier.citationOR Spectrum, 2017; 39(4):1055-1069-
dc.identifier.issn0171-6468-
dc.identifier.issn1436-6304-
dc.identifier.urihttp://hdl.handle.net/2440/113819-
dc.description.abstractIn this paper, we propose a higher-order interactive hidden Markov model, which incorporates both the feedback effects of observable states on hidden states and their mutual long-term dependence. The key idea of this model is to assume the probability laws governing both the observable and hidden states can be written as a pair of higher-order stochastic difference equations. We also present an efficient procedure, a heuristic algorithm, to estimate the hidden states of the chain and the model parameters. Real applications in SSE Composite Index data and default data are given to demonstrate the effectiveness of our proposed model and corresponding estimation method.-
dc.description.statementofresponsibilityDong-Mei Zhu, Wai-Ki Ching, Robert J. Elliott, Tak-Kuen Siu, Lianmin Zhang-
dc.language.isoen-
dc.publisherSpringer-
dc.rights© Springer-Verlag GmbH Germany 2017-
dc.source.urihttp://dx.doi.org/10.1007/s00291-017-0484-0-
dc.subjectInteractive hidden Markov model; hidden Markov model; feedback effect; stochastic difference equations-
dc.titleA Higher-order interactive hidden Markov model and its applications-
dc.typeJournal article-
dc.identifier.doi10.1007/s00291-017-0484-0-
pubs.publication-statusPublished-
Appears in Collections:Aurora harvest 8
Mathematical Sciences publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.