Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/113819
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Type: Journal article
Title: A Higher-order interactive hidden Markov model and its applications
Author: Zhu, D.
Ching, W.
Elliott, R.
Siu, T.
Zhang, L.
Citation: OR Spectrum, 2017; 39(4):1055-1069
Publisher: Springer
Issue Date: 2017
ISSN: 0171-6468
1436-6304
Statement of
Responsibility: 
Dong-Mei Zhu, Wai-Ki Ching, Robert J. Elliott, Tak-Kuen Siu, Lianmin Zhang
Abstract: In 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.
Keywords: Interactive hidden Markov model; hidden Markov model; feedback effect; stochastic difference equations
Rights: © Springer-Verlag GmbH Germany 2017
RMID: 0030090903
DOI: 10.1007/s00291-017-0484-0
Appears in Collections:Mathematical Sciences publications

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