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Hidden Markov modelling of sparse time series from non-volcanic tremor observations

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  • Ting Wang
  • Jiancang Zhuang
  • Kazushige Obara
  • Hiroshi Tsuruoka

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  • Ting Wang & Jiancang Zhuang & Kazushige Obara & Hiroshi Tsuruoka, 2017. "Hidden Markov modelling of sparse time series from non-volcanic tremor observations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 691-715, August.
    • Handle: RePEc:bla:jorssc:v:66:y:2017:i:4:p:691-715
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    File URL: http://hdl.handle.net/10.1111/rssc.12194
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    References listed on IDEAS

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    1. Iain L. MacDonald, 2014. "Numerical Maximisation of Likelihood: A Neglected Alternative to EM?," International Statistical Review, International Statistical Institute, vol. 82(2), pages 296-308, August.
    2. Ting Wang & Mark Bebbington & David Harte, 2012. "Markov-modulated Hawkes process with stepwise decay," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(3), pages 521-544, June.
    3. Filippo Belloc & Mauro Bernardi & Antonello Maruotti & Lea Petrella, 2013. "A dynamic hurdle model for zeroinflated panel count data," Applied Economics Letters, Taylor & Francis Journals, vol. 20(9), pages 837-841, June.
    4. Sarah E. Heaps & Richard J. Boys & Malcolm Farrow, 2015. "Bayesian modelling of rainfall data by using non-homogeneous hidden Markov models and latent Gaussian variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(3), pages 543-568, April.
    5. Ying Hung & Yijie Wang & Veronika Zarnitsyna & Cheng Zhu & C. F. Jeff Wu, 2013. "Hidden Markov Models With Applications in Cell Adhesion Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1469-1479, December.
    6. Rachel MacKay Altman, 2004. "Assessing the Goodness-of-Fit of Hidden Markov Models," Biometrics, The International Biometric Society, vol. 60(2), pages 444-450, June.
    7. Wang, Ting & Bebbington, Mark, 2013. "Identifying anomalous signals in GPS data using HMMs: An increased likelihood of earthquakes?," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 27-44.
    8. Bulla, Jan & Bulla, Ingo & Nenadic, Oleg, 2010. "hsmm -- An R package for analyzing hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 611-619, March.
    9. Liang, Kun & Nettleton, Dan, 2010. "A Hidden Markov Model Approach to Testing Multiple Hypotheses on a Tree-Transformed Gene Ontology Graph," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1444-1454.
    10. Langrock, R. & Zucchini, W., 2011. "Hidden Markov models with arbitrary state dwell-time distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 715-724, January.
    11. C. P. Robert & T. Rydén & D. M. Titterington, 2000. "Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 57-75.
    12. Chantal Guihenneuc-Jouyaux & Sylvia Richardson & Ira M. Longini Jr., 2000. "Modeling Markers of Disease Progression by a Hidden Markov Process: Application to Characterizing CD4 Cell Decline," Biometrics, The International Biometric Society, vol. 56(3), pages 733-741, September.
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    Cited by:

    1. Bountzis, P. & Papadimitriou, E. & Tsaklidis, G., 2020. "Earthquake clusters identification through a Markovian Arrival Process (MAP): Application in Corinth Gulf (Greece)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    2. Amina Shahzadi & Ting Wang & Mark Bebbington & Matthew Parry, 2023. "Inhomogeneous hidden semi-Markov models for incompletely observed point processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(2), pages 253-280, April.
    3. Guglielmo D’Amico & Ada Lika & Filippo Petroni, 2019. "Change point dynamics for financial data: an indexed Markov chain approach," Annals of Finance, Springer, vol. 15(2), pages 247-266, June.

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