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Rectangular latent Markov models for time‐specific clustering, with an analysis of the wellbeing of nations

Author

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  • Gordon Anderson
  • Alessio Farcomeni
  • Maria Grazia Pittau
  • Roberto Zelli

Abstract

A latent Markov model admitting variation in the number of latent states at each time period is introduced. The model facilitates subjects switching latent states at each time period according to an inhomogeneous first‐order Markov process, wherein transition matrices are generally rectangular. As a consequence, latent groups can merge, split or be rearranged. An application analysing the progress of wellbeing of nations, as measured by the three dimensions of the human development index over the last 25 years, illustrates the approach.

Suggested Citation

  • Gordon Anderson & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "Rectangular latent Markov models for time‐specific clustering, with an analysis of the wellbeing of nations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 603-621, April.
    • Handle: RePEc:bla:jorssc:v:68:y:2019:i:3:p:603-621
    DOI: 10.1111/rssc.12312
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    References listed on IDEAS

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    Cited by:

    1. Timo Adam & Roland Langrock & Christian H. Weiß, 2019. "Penalized estimation of flexible hidden Markov models for time series of counts," METRON, Springer;Sapienza Università di Roma, vol. 77(2), pages 87-104, August.
    2. Francesco Bartolucci & Alessio Farcomeni, 2022. "A hidden Markov space–time model for mapping the dynamics of global access to food," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 246-266, January.

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