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http://bura.brunel.ac.uk/handle/2438/10046
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DC Field | Value | Language |
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dc.contributor.author | Ponomareva, K | - |
dc.contributor.author | Date, P | - |
dc.date.accessioned | 2015-02-02T10:02:30Z | - |
dc.date.available | 2015-02-02T10:02:30Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | Applied Mathematics and Computation, 221: 662 - 671, (15 September 2013) | en_US |
dc.identifier.issn | 0096-3003 | - |
dc.identifier.uri | http://www.sciencedirect.com/science/article/pii/S0096300313007339 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/10046 | - |
dc.description.abstract | In this paper we present some new results related to the higher order sigma point filter (HOSPoF), introduced in [1] for filtering nonlinear multivariate time series. This paper makes two distinct contributions. Firstly, we propose a new algorithm to generate a discrete statistical distribution to match exactly a specified mean vector, a specified covariance matrix, the average of specified marginal skewness and the average of specified marginal kurtosis. Both the sigma points and the probability weights are given in closed-form and no numerical optimization is required. Combined with HOSPoF, this random sigma point generation algorithm provides a new method for generating proposal density which propagates the information about higher order moments. A numerical example on nonlinear, multivariate time series involving real financial market data demonstrates the utility of this new algorithm. Secondly, we show that HOSPoF achieves a higher order estimation accuracy as compared to UKF for smooth scalar nonlinearities. We believe that this new filter provides a new and powerful alternative heuristic to existing filtering algorithms and is useful especially in econometrics and in engineering applications. | en_US |
dc.language | eng | - |
dc.language.iso | en | en_US |
dc.subject | Moment matching | en_US |
dc.subject | Nonlinear time series | en_US |
dc.subject | Sigma point filters | en_US |
dc.subject | State estimation | en_US |
dc.title | Higher order sigma point filter: A new heuristic for nonlinear time series filtering | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/j.amc.2013.06.084 | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Mathematics | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Mathematics/Mathematical Sciences | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/Brunel Business School - URCs and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/Brunel Business School - URCs and Groups/Centre for Research into Entrepreneurship, International Business and Innovation in Emerging Markets | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Brunel Institute for Ageing Studies | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Brunel Institute of Cancer Genetics and Pharmacogenomics | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Centre for Systems and Synthetic Biology | - |
Appears in Collections: | Dept of Mathematics Research Papers |
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