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Article: Efficient algorithm for computing maximum likelihood estimates in linear transformation models

TitleEfficient algorithm for computing maximum likelihood estimates in linear transformation models
Authors
KeywordsConstrained optimization
Nonparametric maximum likelihood estimation
Proportional hazards model
Proportional odds model
Recursive formula
Issue Date2006
Citation
Journal Of Computational And Graphical Statistics, 2006, v. 15 n. 1, p. 228-245 How to Cite?
AbstractLinear transformation models, which have been extensively studied in survival analysis, include the two special cases: the proportional hazards model and the proportional odds model. Nonparametric maximum likelihood estimation is usually used to derive the efficient estimators. However, due to the large number of nuisance parameters, calculation of the nonparametric maximum likelihood estimator is difficult in practice, except for the proportional hazards model. We propose an efficient algorithm for computing the maximum likelihood estimates, where the dimensionality of the parameter space is dramatically reduced so that only a finite number of equations need to be solved. Moreover, the asymptotic variance is automatically estimated in the computing procedure. Extensive simulation studies indicate that the proposed algorithm works very well for linear transformation models. A real example is presented for an illustration of the new methodology. © 2006 American Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/146573
ISSN
2015 Impact Factor: 1.755
2015 SCImago Journal Rankings: 2.321
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYin, Gen_HK
dc.contributor.authorZeng, Den_HK
dc.date.accessioned2012-05-02T08:37:06Z-
dc.date.available2012-05-02T08:37:06Z-
dc.date.issued2006en_HK
dc.identifier.citationJournal Of Computational And Graphical Statistics, 2006, v. 15 n. 1, p. 228-245en_HK
dc.identifier.issn1061-8600en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146573-
dc.description.abstractLinear transformation models, which have been extensively studied in survival analysis, include the two special cases: the proportional hazards model and the proportional odds model. Nonparametric maximum likelihood estimation is usually used to derive the efficient estimators. However, due to the large number of nuisance parameters, calculation of the nonparametric maximum likelihood estimator is difficult in practice, except for the proportional hazards model. We propose an efficient algorithm for computing the maximum likelihood estimates, where the dimensionality of the parameter space is dramatically reduced so that only a finite number of equations need to be solved. Moreover, the asymptotic variance is automatically estimated in the computing procedure. Extensive simulation studies indicate that the proposed algorithm works very well for linear transformation models. A real example is presented for an illustration of the new methodology. © 2006 American Statistical Association.en_HK
dc.languageengen_US
dc.relation.ispartofJournal of Computational and Graphical Statisticsen_HK
dc.subjectConstrained optimizationen_HK
dc.subjectNonparametric maximum likelihood estimationen_HK
dc.subjectProportional hazards modelen_HK
dc.subjectProportional odds modelen_HK
dc.subjectRecursive formulaen_HK
dc.titleEfficient algorithm for computing maximum likelihood estimates in linear transformation modelsen_HK
dc.typeArticleen_HK
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1198/106186006X100542en_HK
dc.identifier.scopuseid_2-s2.0-33645571914en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33645571914&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume15en_HK
dc.identifier.issue1en_HK
dc.identifier.spage228en_HK
dc.identifier.epage245en_HK
dc.identifier.isiWOS:000235953700012-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridZeng, D=8725807700en_HK
dc.identifier.citeulike512337-

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