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Hidden Markov partition models

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  • Farcomeni, Alessio

Abstract

We describe an extension of the hidden Markov model in which the manifest process conditionally follows a partition model. The assumption of local independence for the manifest random variable is thus relaxed to arbitrary dependence. The proposed class generalizes different existing models for discrete and continuous time series, and allows for the finest trading off between bias and variance. The models are fit through an EM algorithm, with the usual recursions for hidden Markov models extended at no additional computational cost.

Suggested Citation

  • Farcomeni, Alessio, 2011. "Hidden Markov partition models," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1766-1770.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:12:p:1766-1770
    DOI: 10.1016/j.spl.2011.07.012
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    References listed on IDEAS

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    1. A. Farcomeni, 2011. "Recapture models under equality constraints for the conditional capture probabilities," Biometrika, Biometrika Trust, vol. 98(1), pages 237-242.
    2. Francesco Bartolucci & Alessio Farcomeni, 2010. "A note on the mixture transition distribution and hidden Markov models," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(2), pages 132-138, March.
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    Cited by:

    1. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 433-465, September.
    2. Väinö Jääskinen & Jie Xiong & Jukka Corander & Timo Koski, 2014. "Sparse Markov Chains for Sequence Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 639-655, September.

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