A multivariate hidden Markov model is proposed for clustering mixed linear and circular time-series data with missing values. The model integrates von Mises and normal densities to describe the distribution that the data take under different latent regimes, with parameters that depend on the evolution of an unobserved Markov chain. Estimation is facilitated by an EM algorithm that treats the states of the latent chain and missing values as different sources of incomplete information. The model is exploited to identify sea regimes from multivariate marine data.
Lagona, F., Picone, M. (2013). A Gaussian-Von Mises Hidden Markov model for clustering multivariate linear-circular data. In I.S. Giudici P. (a cura di), Statistical models for data analysis (pp. 171-179). BERLIN : Springer [10.1007/978-3-319-00032-9_20].
A Gaussian-Von Mises Hidden Markov model for clustering multivariate linear-circular data
LAGONA, Francesco;
2013-01-01
Abstract
A multivariate hidden Markov model is proposed for clustering mixed linear and circular time-series data with missing values. The model integrates von Mises and normal densities to describe the distribution that the data take under different latent regimes, with parameters that depend on the evolution of an unobserved Markov chain. Estimation is facilitated by an EM algorithm that treats the states of the latent chain and missing values as different sources of incomplete information. The model is exploited to identify sea regimes from multivariate marine data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
