The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Monte Carlo Smoothing for Nonlinear Time Series
Abstract
We develop methods for performing smoothing computations in general state-space models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are presented for generation of sample realizations of historical state sequences. This is carried out in a forward-filtering backward-smoothing procedure that can be viewed as the nonlinear, non-Gaussian counterpart of standard Kalman filter-based simulation smoothers in the linear Gaussian case. Convergence in the mean squared error sense of the smoothed trajectories is proved, showing the validity of our proposed method. The methods are tested in a substantial application for the processing of speech signals represented by a time-varying autoregression and parameterized in terms of time-varying partial correlation coefficients, comparing the results of our algorithm with those from a simple smoother based on the filtered trajectories.
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Cites
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993) · IEE Proceedings F Radar and Signal Processing · doi:10.1049/ip-f-2.1993.0015 · Cited by 7,550
Sequential Monte Carlo Methods in Practice
(2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,366
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
Viterbi, A. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1054010 · Cited by 6,759
On sequential Monte Carlo sampling methods for Bayesian filtering
Doucet, A., Godsill, S., & Andrieu, C. (2000) · Statistics and Computing · doi:10.1023/a:1008935410038 · Cited by 4,411
Self-citationDiscrete-Time Processing of Speech Signals
Deller, J. R., Hansen, J. H. L., & Proakis, J. G. (1999) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470544402 · Cited by 2,458
Filtering via Simulation: Auxiliary Particle Filters
Pitt, M. K., & Shephard, N. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10474153 · Cited by 2,438
Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models
Kitagawa, G. (1996) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1996.10474692 · Cited by 2,404
Sequential Monte Carlo Methods for Dynamic Systems
Liu, J. S., & Chen, R. (1998) · Journal of the American Statistical Association · doi:10.1080/01621459.1998.10473765 · Cited by 2,191
On Gibbs sampling for state space models
CARTER, C. K., & KOHN, R. (1994) · Biometrika · doi:10.1093/biomet/81.3.541 · Cited by 1,917
Bayesian Forecasting and Dynamic Models
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Cited by
Parameter estimation for differential equations: a generalized smoothing approach
Ramsay, J. O., Hooker, G., Campbell, D., & Cao, J. (2007) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2007.00610.x · Cited by 613
A Survey of Sequential Monte Carlo Methods for Economics and Finance
Creal, D. (2012) · Econometric Reviews · doi:10.1080/07474938.2011.607333 · Cited by 301
General duality between optimal control and estimation
Todorov, E. (2008) · 2008 47th IEEE Conference on Decision and Control · doi:10.1109/cdc.2008.4739438 · Cited by 248
Models and Algorithms for Tracking of Maneuvering Objects Using Variable Rate Particle Filters
Godsill, S. J., Vermaak, J., Ng, W., & Li, J. F. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2007.894708 · Cited by 81
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Metadata
- Authors
- Godsill, S. J., Doucet, A., & West, M.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2004-03
- Volume
- 99
- Issue
- 465
- Pages
- 156-168
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1988827501
- Cited by
- 543
- References
- 26
- FWCI
- 16.40
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
