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Article: Sampling Properties of Color Independent Component Analysis

TitleSampling Properties of Color Independent Component Analysis
Authors
KeywordsBlind source separation
Multivariate analysis
Spectral density estimation
Time series
Whittle likelihood
Issue Date2021
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmva
Citation
Journal of Multivariate Analysis, 2021, v. 181, p. article no. 104692 How to Cite?
AbstractIndependent Component Analysis (ICA) offers an effective data-driven approach for blind source extraction encountered in many signal and image processing problems. Although many ICA methods have been developed, they have received relatively little attention in the statistics literature, especially in terms of rigorous theoretical investigation for statistical inference. The current paper aims at narrowing this gap and investigates the statistical sampling properties of the colorICA (cICA) method. The cICA incorporates the correlation structure within sources through parametric time series models in the frequency domain and outperforms several existing ICA alternatives numerically. We establish the consistency and asymptotic normality of the cICA estimates, which then enables statistical inference based on the estimates. These asymptotic properties are further validated using simulation studies.
Persistent Identifierhttp://hdl.handle.net/10722/290494
ISSN
2023 Impact Factor: 1.4
2023 SCImago Journal Rankings: 0.837
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLee, S-
dc.contributor.authorShen, H-
dc.contributor.authorTruong, Y-
dc.date.accessioned2020-11-02T05:43:02Z-
dc.date.available2020-11-02T05:43:02Z-
dc.date.issued2021-
dc.identifier.citationJournal of Multivariate Analysis, 2021, v. 181, p. article no. 104692-
dc.identifier.issn0047-259X-
dc.identifier.urihttp://hdl.handle.net/10722/290494-
dc.description.abstractIndependent Component Analysis (ICA) offers an effective data-driven approach for blind source extraction encountered in many signal and image processing problems. Although many ICA methods have been developed, they have received relatively little attention in the statistics literature, especially in terms of rigorous theoretical investigation for statistical inference. The current paper aims at narrowing this gap and investigates the statistical sampling properties of the colorICA (cICA) method. The cICA incorporates the correlation structure within sources through parametric time series models in the frequency domain and outperforms several existing ICA alternatives numerically. We establish the consistency and asymptotic normality of the cICA estimates, which then enables statistical inference based on the estimates. These asymptotic properties are further validated using simulation studies.-
dc.languageeng-
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmva-
dc.relation.ispartofJournal of Multivariate Analysis-
dc.subjectBlind source separation-
dc.subjectMultivariate analysis-
dc.subjectSpectral density estimation-
dc.subjectTime series-
dc.subjectWhittle likelihood-
dc.titleSampling Properties of Color Independent Component Analysis-
dc.typeArticle-
dc.identifier.emailShen, H: haipeng@hku.hk-
dc.identifier.authorityShen, H=rp02082-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jmva.2020.104692-
dc.identifier.scopuseid_2-s2.0-85096221388-
dc.identifier.hkuros318189-
dc.identifier.volume181-
dc.identifier.spagearticle no. 104692-
dc.identifier.epagearticle no. 104692-
dc.identifier.isiWOS:000592400900013-
dc.publisher.placeUnited States-
dc.identifier.issnl0047-259X-

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