Motivated by segmentation issues in studies of sea current circulation, we describe a hidden Markov random field for the analysis of spatial cylindrical data, i.e. bivariate spatial series of angles and intensities. The model is based on a mixture of cylindrical densities, whose parameters vary across space according to a latent Markov field. It enables segmentation of the data within a finite number of latent classes that represent the conditional distributions of the data under specific environmental conditions, simultaneously accounting for unobserved heterogeneity and spatial auto-correlation. Further, it parsimoniously accommodates specific features of environmental cylindrical data, such as circularâlinear correlation, multimodality and skewness. Because of the numerical intractability of the likelihood function, estimation of the parameters is based on composite likelihood methods and essentially reduces to a computationally efficient expectationâmaximization algorithm that iteratively alternates the maximization of a weighted composite likelihood function with weights updating. These methods are tested on simulations and exploited to segment the sea surface of the Gulf of Naples by means of meaningful circulation regimes.
Segmentation of sea current fields by cylindrical hidden Markov models: a composite likelihood approach
Zambianchi, Enrico
2018-01-01
Abstract
Motivated by segmentation issues in studies of sea current circulation, we describe a hidden Markov random field for the analysis of spatial cylindrical data, i.e. bivariate spatial series of angles and intensities. The model is based on a mixture of cylindrical densities, whose parameters vary across space according to a latent Markov field. It enables segmentation of the data within a finite number of latent classes that represent the conditional distributions of the data under specific environmental conditions, simultaneously accounting for unobserved heterogeneity and spatial auto-correlation. Further, it parsimoniously accommodates specific features of environmental cylindrical data, such as circularâlinear correlation, multimodality and skewness. Because of the numerical intractability of the likelihood function, estimation of the parameters is based on composite likelihood methods and essentially reduces to a computationally efficient expectationâmaximization algorithm that iteratively alternates the maximization of a weighted composite likelihood function with weights updating. These methods are tested on simulations and exploited to segment the sea surface of the Gulf of Naples by means of meaningful circulation regimes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.