Fast smoothing in switching approximations of non-linear and non-Gaussian models

Ivan Gorynin (1, 2, 3) , Stéphane Derrode (4) , Emmanuel Monfrini (5, 1, 2, 3) , Wojciech Pieczynski (5, 1, 2, 3)

1 TSP - CITI - Communications, Images et Traitement de l'Information
2 SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
3 CNRS - Centre National de la Recherche Scientifique
4 imagine - Extraction de Caractéristiques et Identification
5 TIPIC-SAMOVAR - Traitement de l'Information Pour Images et Communications

Statistical smoothing in general non-linear non-Gaussian systems is a challenging problem. A new smoothing method based on approximating the original system by a recent switching model has been introduced. Such switching model allows fast and optimal smoothing. The new algorithm is validated through an application on stochastic volatility and dynamic beta models. Simulation experiments indicate its remarkable performances and low processing cost. In practice, the proposed approach can overcome the limitations of particle smoothing methods and may apply where their usage is discarded.

Mots clés: Smoothing in non-linear systems; Stochastic volatility; Optimal statistical smoother; Conditionally Gaussian linear state-space models; Conditionally Markov switching hidden linear models

Domaines: Traitement du signal et de l'image [eess.SP]

Ivan Gorynin, Stéphane Derrode, Emmanuel Monfrini, Wojciech Pieczynski. Fast smoothing in switching approximations of non-linear and non-Gaussian models. Computational Statistics and Data Analysis, 2017, 114, pp.38 - 46. ⟨10.1016/j.csda.2017.04.007⟩. ⟨hal-01540474⟩