Model selection in hidden Markov models : a simulation study

Costa, Michele ; De Angelis, Luca (2010) Model selection in hidden Markov models : a simulation study. Bologna, IT: Dipartimento di Scienze Statistiche "Paolo Fortunati", Alma Mater Studiorum Università di Bologna, p. 15. DOI 10.6092/unibo/amsacta/2909. In: Quaderni di Dipartimento. Serie Ricerche ISSN 1973-9346.
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A review of model selection procedures in hidden Markov models reveals contrasting evidence about the reliability and the precision of the most commonly used methods. In order to evaluate and compare existing proposals, we develop a Monte Carlo experiment which allows a powerful insight on the behaviour of the most widespread model selection methods. We find that the number of observations, the conditional state-dependent probabilities, and the latent transition matrix are the main factors influencing information criteria and likelihood ratio test results. We also find evidence that, for shorter univariate time series, AIC strongly outperforms BIC.

Document type
Monograph (Working Paper)
Costa, Michele
De Angelis, Luca
Model selection procedure, Hidden Markov model, Monte Carlo experiment, information criteria, likelihood ratio test. Selezione del modello, Modello markoviano latente, Esperimento Monte Carlo, Criterio di informazione, Test del rapporto di verosimiglianza.
Deposit date
15 Dec 2010 13:15
Last modified
16 May 2011 12:15

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