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Journal article

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 co...

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Publication status:
Published

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Publisher copy:
10.1198/016214504000000151

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Journal:
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume:
99
Issue:
465
Pages:
156-168
Publication date:
2004-03-01
DOI:
EISSN:
1537-274X
ISSN:
0162-1459
Source identifiers:
190594
Language:
English
Keywords:
Pubs id:
pubs:190594
UUID:
uuid:a8793c16-500e-47f3-8b63-139ab6af6001
Local pid:
pubs:190594
Deposit date:
2012-12-19

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