Efficient sequential Monte Carlo with multiple proposals and control variates

Li, Wentao and Chen, Rong and Tan, Zhiqiang (2016) Efficient sequential Monte Carlo with multiple proposals and control variates. Journal of the American Statistical Association, 111 (513). pp. 298-313. ISSN 0162-1459

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Abstract

Sequential Monte Carlo is a useful simulation-based method for on-line 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 paper proposes an efficient method of using multiple proposals in combination with control variates. The likelihood approach of Tan (2004) likelihood 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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the American Statistical Association
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
73689
Deposited By:
Deposited On:
18 Jun 2015 05:41
Refereed?:
Yes
Published?:
Published
Last Modified:
28 Apr 2020 03:13