Motivated by segmentation issues in marine studies, a novel hiddenMarkov model is proposed for the analysis of cylindrical space-time series, thatis, bivariate space-time series of intensities and angles. The model is a multilevelmixture of cylindrical densities, where the parameters of the mixture vary at thespatial level according to a latent Markov random field, while the parameters of thehidden Markov random field evolve at the temporal level according to the states of ahidden Markov chain. Due to the numerical intractability of the likelihood function,parameters are estimated by a computationally efficient EM algorithm based on thespecification of a weighted composite likelihood. The proposal is tested in a casestudy that involves speeds and directions of marine currents in the Gulf of Naples.

Lagona, F. (2018). A multilevel hidden Markov model for space-time cylindrical data. In E.B. Antonino Abbruzzo (a cura di), Proceedings SIS 2018 (pp. 367-372). Pearson.

A multilevel hidden Markov model for space-time cylindrical data

lagona
2018

Abstract

Motivated by segmentation issues in marine studies, a novel hiddenMarkov model is proposed for the analysis of cylindrical space-time series, thatis, bivariate space-time series of intensities and angles. The model is a multilevelmixture of cylindrical densities, where the parameters of the mixture vary at thespatial level according to a latent Markov random field, while the parameters of thehidden Markov random field evolve at the temporal level according to the states of ahidden Markov chain. Due to the numerical intractability of the likelihood function,parameters are estimated by a computationally efficient EM algorithm based on thespecification of a weighted composite likelihood. The proposal is tested in a casestudy that involves speeds and directions of marine currents in the Gulf of Naples.
9788891910233
Lagona, F. (2018). A multilevel hidden Markov model for space-time cylindrical data. In E.B. Antonino Abbruzzo (a cura di), Proceedings SIS 2018 (pp. 367-372). Pearson.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/362491
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