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Article: A smoothed bootstrap test for independence based on mutual information

TitleA smoothed bootstrap test for independence based on mutual information
Authors
Wu, EHCYu, PLHLi, WK
Issue Date2009
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 2009, v. 53 n. 7, p. 2524-2536 How to Cite?
DOI: http://dx.doi.org/10.1016/j.csda.2008.11.032
AbstractA test for independence of multivariate time series based on the mutual information measure is proposed. First of all, a test for independence between two variables based on i.i.d. (time-independent) data is constructed and is then extended to incorporate higher dimensions and strictly stationary time series data. The smoothed bootstrap method is used to estimate the null distribution of mutual information. The experimental results reveal that the proposed smoothed bootstrap test performs better than the existing tests and can achieve high powers even for moderate dependence structures. Finally, the proposed test is applied to assess the actual independence of components obtained from independent component analysis (ICA). © 2008 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/59884
ISSN
0167-9473
2023 Impact Factor: 1.5
2023 SCImago Journal Rankings: 1.008
ISI Accession Number ID
WOS:000264907600009
Funding AgencyGrant Number
Hong Kong Research Grant CouncilHKU7036/06P
Funding Information:

The authors would like to thank the editor, the associate editor, the two reviewers and Stephen M.S. Lee for their valuable comments and suggestions which greatly improved the paper. W.K. Li's research is partially supported by the Hong Kong Research Grant Council Grant HKU7036/06P.

References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorWu, EHCen_HK
dc.contributor.authorYu, PLHen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-05-31T03:59:23Z-
dc.date.available2010-05-31T03:59:23Z-
dc.date.issued2009en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 2009, v. 53 n. 7, p. 2524-2536en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/59884-
dc.description.abstractA test for independence of multivariate time series based on the mutual information measure is proposed. First of all, a test for independence between two variables based on i.i.d. (time-independent) data is constructed and is then extended to incorporate higher dimensions and strictly stationary time series data. The smoothed bootstrap method is used to estimate the null distribution of mutual information. The experimental results reveal that the proposed smoothed bootstrap test performs better than the existing tests and can achieve high powers even for moderate dependence structures. Finally, the proposed test is applied to assess the actual independence of components obtained from independent component analysis (ICA). © 2008 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csdaen_HK
dc.relation.ispartofComputational Statistics and Data Analysisen_HK
dc.titleA smoothed bootstrap test for independence based on mutual informationen_HK
dc.typeArticleen_HK
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.csda.2008.11.032en_HK
dc.identifier.scopuseid_2-s2.0-61849125855en_HK
dc.identifier.hkuros154843en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-61849125855&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume53en_HK
dc.identifier.issue7en_HK
dc.identifier.spage2524en_HK
dc.identifier.epage2536en_HK
dc.identifier.eissn1872-7352-
dc.identifier.isiWOS:000264907600009-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridWu, EHC=25958488900en_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.citeulike11822277-
dc.identifier.issnl0167-9473-

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