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Hidden Markov Models With Applications in Cell Adhesion Experiments

Author

Listed:
  • Ying Hung
  • Yijie Wang
  • Veronika Zarnitsyna
  • Cheng Zhu
  • C. F. Jeff Wu

Abstract

Estimation of the number of hidden states is challenging in hidden Markov models. Motivated by the analysis of a specific type of cell adhesion experiments, a new framework based on a hidden Markov model and double penalized order selection is proposed. The order selection procedure is shown to be consistent in estimating the number of states. A modified expectation--maximization algorithm is introduced to efficiently estimate parameters in the model. Simulations show that the proposed framework outperforms existing methods. Applications of the proposed methodology to real data demonstrate the accuracy of estimating receptor--ligand bond lifetimes and waiting times which are essential in kinetic parameter estimation. Supplementary materials for this article are available online.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:504:p:1469-1479
    DOI: 10.1080/01621459.2013.836973
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    Cited by:

    1. Lin, Yiqi & Song, Xinyuan, 2022. "Order selection for regression-based hidden Markov model," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    2. 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.
    3. Zhou, Jie & Song, Xinyuan & Sun, Liuquan, 2020. "Continuous time hidden Markov model for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 179(C).

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