Smoothing algorithms for state–space models
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DOI: 10.1007/s10463-009-0236-2
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References listed on IDEAS
- Rong Chen & Jun S. Liu, 2000. "Mixture Kalman filters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 493-508.
- Paul Fearnhead & Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Particle filters for partially observed diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 755-777, September.
- Christophe Andrieu & Arnaud Doucet, 2002. "Particle filtering for partially observed Gaussian state space models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 827-836, October.
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Cited by:
- Persing, Adam & Jasra, Ajay, 2013. "Likelihood computation for hidden Markov models via generalized two-filter smoothing," Statistics & Probability Letters, Elsevier, vol. 83(5), pages 1433-1442.
- repec:bla:istatr:v:83:y:2015:i:3:p:405-435 is not listed on IDEAS
- Gorynin, Ivan & Derrode, Stéphane & Monfrini, Emmanuel & Pieczynski, Wojciech, 2017. "Fast smoothing in switching approximations of non-linear and non-Gaussian models," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 38-46.
- Genshiro Kitagawa, 2014. "Computational aspects of sequential Monte Carlo filter and smoother," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 443-471, June.
- Parfait Munezero, 2022. "Efficient particle smoothing for Bayesian inference in dynamic survival models," Computational Statistics, Springer, vol. 37(2), pages 975-994, April.
- António A. F. Santos, 2015. "On the Forecasting of Financial Volatility Using Ultra-High Frequency Data," GEMF Working Papers 2015-17, GEMF, Faculty of Economics, University of Coimbra.
- repec:wyi:journl:002173 is not listed on IDEAS
- Nicolas Chopin & Mathieu Gerber, 2017. "Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes," Working Papers 2017-35, Center for Research in Economics and Statistics.
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Keywords
Sequential Monte Carlo; Two-filter smoothing; State–space models; Rao-Blackwellisation; Non-linear diffusion; Parameter estimation;All these keywords.
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