A note on the mixture transition distribution and hidden Markov models
We discuss an interpretation of the mixture transition distribution (MTD) for discrete-valued time series which is based on a sequence of independent latent variables which are occasion-specific. We show that, by assuming that this latent process follows a first order Markov Chain, MTD can be generalized in a sensible way. A class of models results which also includes the hidden Markov model (HMM). For these models we outline an EM algorithm for the maximum likelihood estimation which exploits recursions developed within the HMM literature. As an illustration, we provide an example based on the analysis of stock market data referred to different American countries. Copyright Copyright 2010 Blackwell Publishing Ltd
Year of publication: |
2010
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Authors: | Bartolucci, Francesco ; Farcomeni, Alessio |
Published in: |
Journal of Time Series Analysis. - Wiley Blackwell, ISSN 0143-9782. - Vol. 31.2010, 2, p. 132-138
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Publisher: |
Wiley Blackwell |
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