Journal article icon

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...

Expand abstract
Publication status:
Published

Actions


Access Document


Publisher copy:
10.1198/016214504000000151

Authors


More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Role:
Author
Journal:
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume:
99
Issue:
465
Pages:
156-168
Publication date:
2004-03-05
DOI:
EISSN:
1537-274X
ISSN:
0162-1459
URN:
uuid:a8793c16-500e-47f3-8b63-139ab6af6001
Source identifiers:
190594
Local pid:
pubs:190594

Terms of use


Metrics


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP