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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Paravee Maneejuk | en_US |
dc.contributor.author | Woraphon Yamaka | en_US |
dc.contributor.author | Songsak Sriboonchitta | en_US |
dc.date.accessioned | 2018-09-05T04:26:15Z | - |
dc.date.available | 2018-09-05T04:26:15Z | - |
dc.date.issued | 2018-01-01 | en_US |
dc.identifier.issn | 16113349 | en_US |
dc.identifier.issn | 03029743 | en_US |
dc.identifier.other | 2-s2.0-85043980870 | en_US |
dc.identifier.other | 10.1007/978-3-319-75429-1_26 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85043980870&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/58560 | - |
dc.description.abstract | © 2018, Springer International Publishing AG, part of Springer Nature. This study proposes the mixture Markov-switching autoregressive model, which allows variation in error distribution across different regimes. This model is generalized from the ordinary MS-AR model owing to two considerations, but related to each other. First, we have concern about the mixture of distributions or populations, which often prevails in economic time series. Second, when using the MS models to analyse economic fluctuation, we doubt if each regime in the model can have distinct distribution. All of these concerns are addressed by an empirical study. | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Mathematics | en_US |
dc.title | A Markov-Switching Model with Mixture Distribution Regimes | en_US |
dc.type | Book Series | en_US |
article.title.sourcetitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_US |
article.volume | 10758 LNAI | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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