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A Gaussian–Von Mises Hidden Markov Model for Clustering Multivariate Linear-Circular Data

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Statistical Models for Data Analysis

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.

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Author information

Authors and Affiliations

  1. University Roma Tre, Rome, Italy

    Francesco Lagona & Marco Picone

  2. Marine Service, ISPRA, Italy

    Marco Picone

Authors
  1. Francesco Lagona
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  2. Marco Picone
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Corresponding author

Correspondence to Francesco Lagona .

Editor information

Editors and Affiliations

  1. Department of Economics, and Management, University of Pavia, Via San Felice 7, Pavia, 27100, Italy

    Paolo Giudici

  2. Department of Economics, and Business, University of Catania, Corso Italia 55, Catania, 95129, Italy

    Salvatore Ingrassia

  3. , Department of Statistics, University of Rome "La Sapienza", Piazzale Aldo Moro 5, Rome, 00185, Italy

    Maurizio Vichi

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© 2013 Springer International Publishing Switzerland

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Lagona, F., Picone, M. (2013). A Gaussian–Von Mises Hidden Markov Model for Clustering Multivariate Linear-Circular Data. In: Giudici, P., Ingrassia, S., Vichi, M. (eds) Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00032-9_20

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