Finding the number of latent states in hidden Markov models using information criteria-Reference-Cited by-同舟云学术

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Finding the number of latent states in hidden Markov models using information criteria

Published:2023-11-22 Issue:4 Volume:30 Page:797-825
ISSN:1352-8505
Container-title:Environmental and Ecological Statistics
language:en
Short-container-title:Environ Ecol Stat

Author:

Buckby Jodie,Wang Ting,Fletcher David,Zhuang Jiancang,Takeo Akiko,Obara Kazushige

Funder

Ministry of Business, Innovation and Employment

Japan Society for the Promotion of Science

Publisher

Springer Science and Business Media LLC

Subject

Statistics, Probability and Uncertainty,General Environmental Science,Statistics and Probability

Link

https://link.springer.com/content/pdf/10.1007/s10651-023-00584-5.pdf

Reference46 articles.

1. Akaike H (1974) Information theory and an extension of the maximum likelihood principle. IEEE Trans Autom Control 19(6):716–723

2. Bacci S, Pandolfi S, Pennoni F (2014) A comparison of some criteria for states selection in the latent Markov model for longitudinal data. Adv Data Anal Classif 8:125–145

3. Baudry J, Raftery AE, Celeux G et al (2010) Combining mixture components for clustering. J Comput Graph Stat 19(2):332–353

4. Bengtsson T, Cavanaugh J (2006) An improved Akaike information criterion for state-space model selection. Comput Stat Data Anal 50:2635–2654

5. Biernacki C, Govaert G (1997) Using the classification likelihood to choose the number of clusters. Comput Sci Stat 29:451–457

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