Penalised hidden semi-Markov models with flexible sojourn-time distributions
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Alessio Farcomeni [1]
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[1] University of Rome
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- Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 35, Nº. 1, 2026, págs. 98-116
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
In the class of hidden semi-Markov models with non-parametric sojourn-time distribution, we present a framework that penalises the latter with respect to its departure from a parametric base kernel. The penalised approach explicitly bridges parametric and non-parametric assumptions for the sojourn-time distributions, also in terms of the effective number of parameters. Inference is obtained via an expectation–maximisation algorithm. For Ridge-type penalties, we reduce the M step to a univariate optimisation problem, thereby greatly improving the computational burden. The penalty parameter is chosen using a computationally efficient Akaike-type information criterion. We illustrate our method with a simulation study, and a real data application to a large number of time series measuring traffic flow in several spots of five European cities. In the real data example, penalised hidden semi-Markov models are often preferred to hidden Markov models, and to hidden semi-Markov models with parametric and non-parametric sojourn-time specifications.
