Bayesian hidden Markov modelling using circular‐linear general projected normal distribution

@article{Mastrantonio2014BayesianHM,
  title={Bayesian hidden Markov modelling using circular‐linear general projected normal distribution},
  author={Gianluca Mastrantonio and Antonello Maruotti and Giovanna Jona-Lasinio},
  journal={Environmetrics},
  year={2014},
  volume={26},
  pages={145 - 158}
}
We introduce a multivariate hidden Markov model to jointly cluster time‐series observations with different support, that is, circular and linear. Relying on the general projected normal distribution, our approach allows for bimodal and/or skewed cluster‐specific distributions for the circular variable. Furthermore, we relax the independence assumption between the circular and linear components observed at the same time. Such an assumption is generally used to alleviate the computational burden… 
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32 Citations
Background Citations
15
Methods Citations
8

32 Citations

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Analyzing longitudinal circular data by projected normal models: a semi-parametric approach based on finite mixture models
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    Environmental and Ecological Statistics
  • 2015
The analysis of circular data has been recently the focus of a wide range of literature, with the general objective of providing reliable parameter estimates in the presence of heterogeneity and/or…
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A hidden Markov model for longitudinal circular data combined with a uniform conditional density on the circle to capture noise observations is proposed, and a set of exogenous covariates is available to affect the evolution of clustering over time.
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A multivariate version of the Gaussian-log Gaussian convolution process is developed which, by virtue of its capacity for capturing skewness, is potentially more flexible than symmetric ones.
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