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Article: Recursive estimation of model parameters with sharp discontinuity in non-stationary air quality data

TitleRecursive estimation of model parameters with sharp discontinuity in non-stationary air quality data
Authors
KeywordsAir Pollution Episode
Intervention Analysis
Kalman Filter
Non-Stationary Time Series
Recursive Estimation And Smoothing
Issue Date2004
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/envsoft
Citation
Environmental Modelling and Software, 2004, v. 19 n. 1, p. 19-25 How to Cite?
AbstractRecursive method of time series filtering and smoothing based on the state-space concept provides a natural approach to the modeling of non-stationary environmental time series. The flexibility of this stochastic formulation allows for a suitable degree of variability in the estimated components, and in this paper we show how it can be extended for handling sharp changes or discontinuities in the model parameters. The approach is based on the time variable parameter version of the well known linear regression model and exploits the suite of recursive Kalman filtering and fixed interval smoothing (FIS) algorithms. The practical utility of the method is demonstrated by an example of modeling of the RSP levels during an episode event. © 2003 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/157857
ISSN
2015 Impact Factor: 4.207
2015 SCImago Journal Rankings: 2.198
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorNg, CNen_US
dc.contributor.authorYan, TLen_US
dc.date.accessioned2012-08-08T08:56:00Z-
dc.date.available2012-08-08T08:56:00Z-
dc.date.issued2004en_US
dc.identifier.citationEnvironmental Modelling and Software, 2004, v. 19 n. 1, p. 19-25en_US
dc.identifier.issn1364-8152en_US
dc.identifier.urihttp://hdl.handle.net/10722/157857-
dc.description.abstractRecursive method of time series filtering and smoothing based on the state-space concept provides a natural approach to the modeling of non-stationary environmental time series. The flexibility of this stochastic formulation allows for a suitable degree of variability in the estimated components, and in this paper we show how it can be extended for handling sharp changes or discontinuities in the model parameters. The approach is based on the time variable parameter version of the well known linear regression model and exploits the suite of recursive Kalman filtering and fixed interval smoothing (FIS) algorithms. The practical utility of the method is demonstrated by an example of modeling of the RSP levels during an episode event. © 2003 Elsevier Ltd. All rights reserved.en_US
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/envsoften_US
dc.relation.ispartofEnvironmental Modelling and Softwareen_US
dc.subjectAir Pollution Episodeen_US
dc.subjectIntervention Analysisen_US
dc.subjectKalman Filteren_US
dc.subjectNon-Stationary Time Seriesen_US
dc.subjectRecursive Estimation And Smoothingen_US
dc.titleRecursive estimation of model parameters with sharp discontinuity in non-stationary air quality dataen_US
dc.typeArticleen_US
dc.identifier.emailNg, CN: cnng@hkucc.hku.hken_US
dc.identifier.authorityNg, CN=rp00606en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/S1364-8152(03)00099-9en_US
dc.identifier.scopuseid_2-s2.0-0842328795en_US
dc.identifier.hkuros92563-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0842328795&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume19en_US
dc.identifier.issue1en_US
dc.identifier.spage19en_US
dc.identifier.epage25en_US
dc.identifier.isiWOS:000187897000003-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridNg, CN=7401705590en_US
dc.identifier.scopusauthoridYan, TL=7102551518en_US

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