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Retracted article: Smoothing with Couplings of Conditional Particle Filters
Harvard University, USA.
Uppsala universitet, Avdelningen för systemteknik, Sweden.
Uppsala universitet, Avdelningen för systemteknik, Sweden.ORCID iD: 0000-0001-5183-234X
2018 (English)In: Journal of the American Statistical Association, ISSN 0162-1459, E-ISSN 1537-274XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

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.

Place, publisher, year, edition, pages
Taylor & Francis , 2018.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-159811DOI: 10.1080/01621459.2018.1505625OAI: oai:DiVA.org:liu-159811DiVA, id: diva2:1344839
Funder
Swedish Foundation for Strategic Research , RIT15-0012Swedish Research Council, 2016-04278Swedish Research Council, 621-2016-06079
Note

Statement of Retraction

We, the Authors, Editors, and Publishers of the Journal of the American Statistical Association, have retracted the following article:

P. E. Jacob, F. Lindsten, T. B. Schön. “Smoothing with Couplings of Conditional Particle Filters,” the Journal of the American Statistical Association. Published Online 6 August 2018. DOI: 10.1080/01621459.2018.1505625.

Following publication on the Latest Articles page of the journal's website, it came to light that there existed a bug in the code used to produce the numbers initially presented in the retracted version. The results themselves remain the same, and not a word will have been changed when the article publishes in final form. The final article will be the version of record in good standing.

We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.

Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22

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