This paper introduces a concomitant-variable hidden semi-Markov model tailored to analyze marine count data in the Venice lagoon. Our model targets {\it acqua alta} events, i.e the exceedances of flooding limits, addressing the prevalent zero counts within the dataset through a fitted zero-inflated Poisson distribution. The data's dynamics are attributed to a discrete set of hidden environmental, risk states, evolving through time following a (non-homogeneous) hidden semi-Markov chain. Furthermore, we extend the conventional hidden semi-Markov approach by introducing regression-dependent state-specific duration parameters, enhancing the model's adaptability and precision in capturing real-world complexities. Our methodology hinges on the maximum likelihood estimation, directly optimizing the log-likelihood function to infer the model's parameters. Through the definition of this novel hidden semi-Markov model, we aim to offer a complete understanding of the intricate interplay between weather states, environmental variables, and the observed marine count data, thus contributing to a nuanced analysis of the Venice lagoon's data.

A zero-inflated hidden semi-Markov model with covariate-dependent sojourn parameters for analysing marine data in the Venice lagoon

Ricciotti, Lorena
;
Pollice, Alessio;
2024-01-01

Abstract

This paper introduces a concomitant-variable hidden semi-Markov model tailored to analyze marine count data in the Venice lagoon. Our model targets {\it acqua alta} events, i.e the exceedances of flooding limits, addressing the prevalent zero counts within the dataset through a fitted zero-inflated Poisson distribution. The data's dynamics are attributed to a discrete set of hidden environmental, risk states, evolving through time following a (non-homogeneous) hidden semi-Markov chain. Furthermore, we extend the conventional hidden semi-Markov approach by introducing regression-dependent state-specific duration parameters, enhancing the model's adaptability and precision in capturing real-world complexities. Our methodology hinges on the maximum likelihood estimation, directly optimizing the log-likelihood function to infer the model's parameters. Through the definition of this novel hidden semi-Markov model, we aim to offer a complete understanding of the intricate interplay between weather states, environmental variables, and the observed marine count data, thus contributing to a nuanced analysis of the Venice lagoon's data.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/528240
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