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Article: Profile empirical likelihood for parametric and semiparametric models

TitleProfile empirical likelihood for parametric and semiparametric models
Authors
KeywordsEfficiency
Empirical Likelihood
Parametric And Semiparametric Models
Profile Likelihood
Issue Date2005
Citation
Annals Of The Institute Of Statistical Mathematics, 2005, v. 57 n. 3, p. 485-505 How to Cite?
AbstractThis paper introduces a profile empirical likelihood and a profile conditionally empirical likelihood to estimate the parameter of interest in the presence of nuisance parameters respectively for the parametric and semiparametric models. It is proven that these methods propose some efficient estimators of parameters of interest in the sense of least-favorable efficiency. Particularly, for the decomposable semiparametric models, an explicit representation for the estimator of parameter of interest is derived from the proposed nonparametric method. These new estimations are different from and more efficient than the existing estimations. Some examples and simulation studies are given to illustrate the theoretical results. © 2005 The Institute of Statistical Mathematics.
Persistent Identifierhttp://hdl.handle.net/10722/172418
ISSN
2015 Impact Factor: 0.768
2015 SCImago Journal Rankings: 0.931
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLin, Len_US
dc.contributor.authorZhu, Len_US
dc.contributor.authorYuen, KCen_US
dc.date.accessioned2012-10-30T06:22:24Z-
dc.date.available2012-10-30T06:22:24Z-
dc.date.issued2005en_US
dc.identifier.citationAnnals Of The Institute Of Statistical Mathematics, 2005, v. 57 n. 3, p. 485-505en_US
dc.identifier.issn0020-3157en_US
dc.identifier.urihttp://hdl.handle.net/10722/172418-
dc.description.abstractThis paper introduces a profile empirical likelihood and a profile conditionally empirical likelihood to estimate the parameter of interest in the presence of nuisance parameters respectively for the parametric and semiparametric models. It is proven that these methods propose some efficient estimators of parameters of interest in the sense of least-favorable efficiency. Particularly, for the decomposable semiparametric models, an explicit representation for the estimator of parameter of interest is derived from the proposed nonparametric method. These new estimations are different from and more efficient than the existing estimations. Some examples and simulation studies are given to illustrate the theoretical results. © 2005 The Institute of Statistical Mathematics.en_US
dc.languageengen_US
dc.relation.ispartofAnnals of the Institute of Statistical Mathematicsen_US
dc.subjectEfficiencyen_US
dc.subjectEmpirical Likelihooden_US
dc.subjectParametric And Semiparametric Modelsen_US
dc.subjectProfile Likelihooden_US
dc.titleProfile empirical likelihood for parametric and semiparametric modelsen_US
dc.typeArticleen_US
dc.identifier.emailYuen, KC: kcyuen@hku.hken_US
dc.identifier.authorityYuen, KC=rp00836en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/BF02509236en_US
dc.identifier.scopuseid_2-s2.0-29344455142en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-29344455142&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume57en_US
dc.identifier.issue3en_US
dc.identifier.spage485en_US
dc.identifier.epage505en_US
dc.identifier.isiWOS:000232494500005-
dc.publisher.placeGermanyen_US
dc.identifier.scopusauthoridLin, L=35315971600en_US
dc.identifier.scopusauthoridZhu, L=7404201068en_US
dc.identifier.scopusauthoridYuen, KC=7202333703en_US

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