General framework and model building in the class of Hidden Mixture Transition Distribution models

Bolano, Danilo and Berchtold, Andre (2016) General framework and model building in the class of Hidden Mixture Transition Distribution models. Computational Statistics and Data Analysis, 93 131-145. doi:10.1016/j.csda.2014.09.011


Author Bolano, Danilo
Berchtold, Andre
Title General framework and model building in the class of Hidden Mixture Transition Distribution models
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
1872-7352
Publication date 2016-01
Sub-type Article (original research)
DOI 10.1016/j.csda.2014.09.011
Volume 93
Start page 131
End page 145
Total pages 15
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2017
Language eng
Formatted abstract
Modeling time series that present non-Gaussian features plays as central role in many fields, including finance, seismology, psychological, and life course studies. The Hidden Mixture Transition Distribution model is an answer to the complexity of such series. The observed heterogeneity can be induced by one or several latent factors, and each level of these factors is related to a different component of the observed process. The time series is then treated as a mixture and the relation between the components is governed by a Markovian latent transition process. This framework generalizes several specifications that appear separately in related literature. Both the expectation and the standard deviation of each component are allowed to be functions of the past of the process. The latent process can be of any order, and can be modeled using a discrete Mixture Transition Distribution. The effects of covariates at the visible and hidden levels are also investigated. One of the main difficulties lies in correctly specifying the structure of the model. Therefore, we propose a hierarchical model selection procedure that exploits the multilevel structure of our approach. Finally, we illustrate the model and the model selection procedure through a real application in social science.
Keyword BIC
Hidden Markov model
Mixture model
Mixture Transition Distribution model
Model selection
Panel data
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Social Research Centre Publications
 
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