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Article: Binary geometric process model for the modeling of longitudinal binary data with trend

TitleBinary geometric process model for the modeling of longitudinal binary data with trend
Authors
KeywordsGeometric process
Longitudinal binary data
Threshold model
Trend data
Issue Date2010
PublisherPhysica-Verlag GmbH und Co.
Citation
Computational Statistics, 2010, v. 25 n. 3, p. 505-536 How to Cite?
AbstractWe propose the Binary Geometric Process (BGP) model for longitudinal binary data with trends. The Geometric Process (GP) model contains two components to capture the dynamics on a trend: the mean of an underlying renewal process and the ratio which measures the direction and strength of the trend. The GP model is extended to binary data using a latent GP. The statistical inference for the BGP models is conducted using the least-square, maximum likelihood (ML) and Bayesian methods. The model is demonstrated through simulation studies and real data analyzes. Results reveal that all estimators perform satisfactorily and that the ML estimator performs the best. Moreover the BGP model is better than the ordinary logistic regression model. © 2010 The Author(s).
Persistent Identifierhttp://hdl.handle.net/10722/124058
ISSN
2015 Impact Factor: 0.52
2015 SCImago Journal Rankings: 0.685
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, JSKen_HK
dc.contributor.authorLeung, DYPen_HK
dc.date.accessioned2010-10-19T04:36:51Z-
dc.date.available2010-10-19T04:36:51Z-
dc.date.issued2010en_HK
dc.identifier.citationComputational Statistics, 2010, v. 25 n. 3, p. 505-536en_HK
dc.identifier.issn0943-4062en_HK
dc.identifier.urihttp://hdl.handle.net/10722/124058-
dc.description.abstractWe propose the Binary Geometric Process (BGP) model for longitudinal binary data with trends. The Geometric Process (GP) model contains two components to capture the dynamics on a trend: the mean of an underlying renewal process and the ratio which measures the direction and strength of the trend. The GP model is extended to binary data using a latent GP. The statistical inference for the BGP models is conducted using the least-square, maximum likelihood (ML) and Bayesian methods. The model is demonstrated through simulation studies and real data analyzes. Results reveal that all estimators perform satisfactorily and that the ML estimator performs the best. Moreover the BGP model is better than the ordinary logistic regression model. © 2010 The Author(s).en_HK
dc.languageengen_HK
dc.publisherPhysica-Verlag GmbH und Co.en_HK
dc.relation.ispartofComputational Statisticsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsThe original publication is available at www.springerlink.comen_HK
dc.subjectGeometric processen_HK
dc.subjectLongitudinal binary dataen_HK
dc.subjectThreshold modelen_HK
dc.subjectTrend dataen_HK
dc.titleBinary geometric process model for the modeling of longitudinal binary data with trenden_HK
dc.typeArticleen_HK
dc.identifier.emailLeung, DYP: dorisl@hkucc.hku.hken_HK
dc.identifier.authorityLeung, DYP=rp00465en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1007/s00180-010-0190-8en_HK
dc.identifier.scopuseid_2-s2.0-77954534665en_HK
dc.identifier.hkuros173788-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77954534665&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume25en_HK
dc.identifier.issue3en_HK
dc.identifier.spage505en_HK
dc.identifier.epage536en_HK
dc.identifier.eissn1613-9658en_HK
dc.identifier.isiWOS:000280074100009-
dc.publisher.placeGermanyen_HK
dc.description.otherSpringer Open Choice, 01 Dec 2010-
dc.identifier.scopusauthoridChan, JSK=24467617500en_HK
dc.identifier.scopusauthoridLeung, DYP=16304486500en_HK
dc.identifier.citeulike7116130-

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