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Robust regularized conditional heteroscedastic hidden semi-Markov models for the analysis of sea levels in the Venice Lagoon

Lorena Ricciotti et al.

Environmental and Ecological Statistics2026https://doi.org/10.1007/s10651-026-00715-8article
ABDC A
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0.50

What the paper says

This paper introduces a novel robust and regularized modeling framework for analyzing sea-level dynamics in the Venice Lagoon. We propose a conditional heteroscedastic hidden semi-Markov model that captures time-varying exposure to flooding events, accounting for time-varying volatility in a regression framework while explicitly modeling state durations. To enhance estimation stability and interpretability, we incorporate regularization techniques and develop a robust estimation procedure to mitigate the influence of outliers, by considering robust conditional distributions as alternatives to the classical Gaussian distribution. The proposed methodology is applied to hourly sea-level data, revealing distinct temporal conditions associated with observed environmental variables.

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https://doi.org/https://doi.org/10.1007/s10651-026-00715-8

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@article{lorena2026,
  title        = {{Robust regularized conditional heteroscedastic hidden semi-Markov models for the analysis of sea levels in the Venice Lagoon}},
  author       = {Lorena Ricciotti et al.},
  journal      = {Environmental and Ecological Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s10651-026-00715-8},
}

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Robust regularized conditional heteroscedastic hidden semi-Markov models for the analysis of sea levels in the Venice Lagoon

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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