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Retracted article: ...
Retracted article: Smoothing with Couplings of Conditional Particle Filters
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- Jacob, Pierre (author)
- Harvard University, USA
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- Lindsten, Fredrik (author)
- Uppsala universitet, Avdelningen för systemteknik, Sweden
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- Schön, Thomas B., Professor, 1977- (author)
- Uppsala universitet, Avdelningen för systemteknik, Sweden
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(creator_code:org_t)
- 2018
- 2018
- English.
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In: Journal of the American Statistical Association. - : Taylor & Francis. - 0162-1459 .- 1537-274X.
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Abstract
Subject headings
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- In state space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and confidence intervals can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly-informative observations, and for a realistic Lotka-Volterra model with an intractable transition density.
Subject headings
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Publication and Content Type
- ref (subject category)
- art (subject category)
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