Bayesian Hidden Markov Modelling Using Circular-Linear General Projected Normal Distribution

Abstract

We introduce a multivariate hidden Markov model to jointly cluster time-series observations with different support, i.e. 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 involved in the parameter estimation step, but it is hard to justify in empirical applications. We carry out a simulation study using different data-generation schemes to investigate model behavior, focusing on well recovering the hidden structure. Finally, the model is used to fit a real data example on a bivariate time series of wind speed and direction.

Environmetrics, (2015)

References in corpus (4)

• Spatial analysis of wave direction data using wrapped Gaussian processes
• Assessment of school performance through a multilevel latent Markov Rasch model
• A two-state mixed hidden Markov model for risky teenage driving behavior
• Parameter Estimation in Hidden Markov Models with Intractable Likelihoods Using Sequential Monte Carlo

Cited by in corpus (3)

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