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

Abstract : 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.
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Computational Statistics & Data Analysis, 2017, 114, pp.38 - 46. <10.1016/j.csda.2017.04.007>
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Soumis le : vendredi 16 juin 2017 - 13:10:41
Dernière modification le : mercredi 28 juin 2017 - 01:10:16

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

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