IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v66y2017i4p691-715.html
   My bibliography  Save this article

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

Author

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

Abstract

No abstract is available for this item.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssc.12194
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maruotti, Antonello & Petrella, Lea & Sposito, Luca, 2021. "Hidden semi-Markov-switching quantile regression for time series," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    2. Zhou, Jie & Song, Xinyuan & Sun, Liuquan, 2020. "Continuous time hidden Markov model for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    3. Shi, Yue & Punzo, Antonio & Otneim, Håkon & Maruotti, Antonello, 2023. "Hidden semi-Markov models for rainfall-related insurance claims," Discussion Papers 2023/17, Norwegian School of Economics, Department of Business and Management Science.
    4. Antonello Maruotti & Antonio Punzo, 2021. "Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies," International Statistical Review, International Statistical Institute, vol. 89(3), pages 447-480, December.
    5. 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.
    6. Pohle, Jennifer & Adam, Timo & Beumer, Larissa T., 2022. "Flexible estimation of the state dwell-time distribution in hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    7. Spezia, Luigi, 2020. "Bayesian variable selection in non-homogeneous hidden Markov models through an evolutionary Monte Carlo method," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    8. Lin, Yiqi & Song, Xinyuan, 2022. "Order selection for regression-based hidden Markov model," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    9. Nicosia, Aurélien & Duchesne, Thierry & Rivest, Louis-Paul & Fortin, Daniel, 2017. "A general hidden state random walk model for animal movement," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 76-95.
    10. Iain L. MacDonald & Brendon M. Lapham, 2016. "Even More Direct Calculation of the Variance of a Maximum Penalized-Likelihood Estimator," The American Statistician, Taylor & Francis Journals, vol. 70(1), pages 114-118, February.
    11. Donatien Hainaut & Franck Moraux, 2019. "A switching self-exciting jump diffusion process for stock prices," Annals of Finance, Springer, vol. 15(2), pages 267-306, June.
    12. Marc Chadeau‐Hyam & Paul S. Clarke & Chantal Guihenneuc‐Jouyaux & Simon N. Cousens & Robert G. Will & Azra C. Ghani, 2010. "An application of hidden Markov models to the French variant Creutzfeldt–Jakob disease epidemic," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 839-853, November.
    13. Roland Langrock & Thomas Kneib & Alexander Sohn & Stacy L. DeRuiter, 2015. "Nonparametric inference in hidden Markov models using P-splines," Biometrics, The International Biometric Society, vol. 71(2), pages 520-528, June.
    14. 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).
    15. Timoth'ee Fabre & Ioane Muni Toke, 2024. "Neural Hawkes: Non-Parametric Estimation in High Dimension and Causality Analysis in Cryptocurrency Markets," Papers 2401.09361, arXiv.org, revised Jan 2024.
    16. Christopher Nam & John Aston & Adam Johansen, 2014. "Parallel sequential Monte Carlo samplers and estimation of the number of states in a Hidden Markov Model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 553-575, June.
    17. Liu, Hefei & Song, Xinyuan & Zhang, Baoxue, 2022. "Varying-coefficient hidden Markov models with zero-effect regions," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    18. Sofia Ruiz-Suarez & Vianey Leos-Barajas & Juan Manuel Morales, 2022. "Hidden Markov and Semi-Markov Models When and Why are These Models Useful for Classifying States in Time Series Data?," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 339-363, June.
    19. Jennifer Pohle & Roland Langrock & Floris M. Beest & Niels Martin Schmidt, 2017. "Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 270-293, September.
    20. Salima El Kolei, 2013. "Parametric estimation of hidden stochastic model by contrast minimization and deconvolution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(8), pages 1031-1081, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssc:v:66:y:2017:i:4:p:691-715. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.