File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1080/01621459.2015.1006364
- Scopus: eid_2-s2.0-84969785132
- WOS: WOS:000376031000028
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Efficient Sequential Monte Carlo With Multiple Proposals and Control Variates
Title | Efficient Sequential Monte Carlo With Multiple Proposals and Control Variates |
---|---|
Authors | |
Keywords | Defensive proposal distribution Importance sampling Regression Auxiliary particle filter |
Issue Date | 2016 |
Citation | Journal of the American Statistical Association, 2016, v. 111, n. 513, p. 298-313 How to Cite? |
Abstract | © 2016 American Statistical Association. Sequential Monte Carlo is a useful simulation-based method for online filtering of state-space models. For certain complex state-space models, a single proposal distribution is usually not satisfactory and using multiple proposal distributions is a general approach to address various aspects of the filtering problem. This article proposes an efficient method of using multiple proposals in combination with control variates. The likelihood approach of Tan (2004) is used in both resampling and estimation. The new algorithm is shown to be asymptotically more efficient than the direct use of multiple proposals and control variates. The guidance for selecting multiple proposals and control variates is also given. Numerical examples are used to demonstrate that the new algorithm can significantly improve over the bootstrap filter and auxiliary particle filter. |
Persistent Identifier | http://hdl.handle.net/10722/267038 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 3.922 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Li, Wentao | - |
dc.contributor.author | Chen, Rong | - |
dc.contributor.author | Tan, Zhiqiang | - |
dc.date.accessioned | 2019-01-31T07:20:20Z | - |
dc.date.available | 2019-01-31T07:20:20Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Journal of the American Statistical Association, 2016, v. 111, n. 513, p. 298-313 | - |
dc.identifier.issn | 0162-1459 | - |
dc.identifier.uri | http://hdl.handle.net/10722/267038 | - |
dc.description.abstract | © 2016 American Statistical Association. Sequential Monte Carlo is a useful simulation-based method for online filtering of state-space models. For certain complex state-space models, a single proposal distribution is usually not satisfactory and using multiple proposal distributions is a general approach to address various aspects of the filtering problem. This article proposes an efficient method of using multiple proposals in combination with control variates. The likelihood approach of Tan (2004) is used in both resampling and estimation. The new algorithm is shown to be asymptotically more efficient than the direct use of multiple proposals and control variates. The guidance for selecting multiple proposals and control variates is also given. Numerical examples are used to demonstrate that the new algorithm can significantly improve over the bootstrap filter and auxiliary particle filter. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of the American Statistical Association | - |
dc.subject | Defensive proposal distribution | - |
dc.subject | Importance sampling | - |
dc.subject | Regression | - |
dc.subject | Auxiliary particle filter | - |
dc.title | Efficient Sequential Monte Carlo With Multiple Proposals and Control Variates | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/01621459.2015.1006364 | - |
dc.identifier.scopus | eid_2-s2.0-84969785132 | - |
dc.identifier.volume | 111 | - |
dc.identifier.issue | 513 | - |
dc.identifier.spage | 298 | - |
dc.identifier.epage | 313 | - |
dc.identifier.eissn | 1537-274X | - |
dc.identifier.isi | WOS:000376031000028 | - |
dc.identifier.issnl | 0162-1459 | - |