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

被引：4

作者：

Gorynin, Ivan ^{[1
]}

Derrode, Stephane ^{[2
]}

Monfrini, Emmanuel ^{[1
]}

Pieczynski, Wojciech ^{[1
]}

机构：

[1] Univ Paris Saclay, CNRS, Telecom Sudparis, SAMOVAR, 9 Rue Charles Fourier, F-91011 Evry, France

[2] Ecole Cent Lyon, LIRIS, CNRS UMR 5205, 36 Av Guy de Collongue, F-69134 Ecully, France

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2017年 / 114卷

关键词：

Smoothing in non-linear systems; Stochastic volatility; Optimal statistical smoother; Conditionally Gaussian linear state-space models; Conditionally Markov switching hidden linear models; STOCHASTIC VOLATILITY MODELS; JUMP;

D O I：

10.1016/j.csda.2017.04.007

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：38 / 46

页数：9

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