A note on kernel density estimation with auxiliary information

It is well-known that the method of empirical likelihood can be employed to get sharper inferences on many functionals of the population distribution by making effective use of auxiliary information available in a nonparametric model. In this paper, we show that in the context of estimating a popula...
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Autor*in:	Zhang, Biao Zhang Biao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Übergeordnetes Werk:	Enthalten in: Communications in statistics / Theory and methods - London : Taylor and Francis, 1982, 27(1998), 1, Seite 1-11
Übergeordnetes Werk:	number:1 ; volume:27 ; year:1998 ; pages:1-11

Links:	Link aufrufen

DOI / URN:	10.1080/03610929808832647

Katalog-ID:	NLEJ253041031

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allfields	10.1080/03610929808832647 doi (DE-627)NLEJ253041031 (TFO)780123990 DE-627 ger DE-627 rda eng Zhang, Biao Zhang Biao verfasserin aut A note on kernel density estimation with auxiliary information 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It is well-known that the method of empirical likelihood can be employed to get sharper inferences on many functionals of the population distribution by making effective use of auxiliary information available in a nonparametric model. In this paper, we show that in the context of estimating a population density function, the modified kernel density estimator which makes use of the knowledge of auxiliary information does not sharpen our inferences on the population density function in the sense that the modified kernel density estimator is first order equivalent to the standard kernel density estimator which does not utilize auxiliary information. Enthalten in Communications in statistics / Theory and methods London : Taylor and Francis, 1982 27(1998), 1, Seite 1-11 Online-Ressource (DE-627)NLEJ252999304 (DE-600)1476983-9 (DE-576)103868100 1532-415X nnns number:1 volume:27 year:1998 pages:1-11 https://www.tib.eu/de/suchen/id/tandf%3A8972df31f69a381755896aa406a8f3d4777ce82b Digitalisierung Deutschlandweit zugänglich ZDB-1-TFO GBV_NL_ARTICLE AR 1 27 1998 1-11
spelling	10.1080/03610929808832647 doi (DE-627)NLEJ253041031 (TFO)780123990 DE-627 ger DE-627 rda eng Zhang, Biao Zhang Biao verfasserin aut A note on kernel density estimation with auxiliary information 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It is well-known that the method of empirical likelihood can be employed to get sharper inferences on many functionals of the population distribution by making effective use of auxiliary information available in a nonparametric model. In this paper, we show that in the context of estimating a population density function, the modified kernel density estimator which makes use of the knowledge of auxiliary information does not sharpen our inferences on the population density function in the sense that the modified kernel density estimator is first order equivalent to the standard kernel density estimator which does not utilize auxiliary information. Enthalten in Communications in statistics / Theory and methods London : Taylor and Francis, 1982 27(1998), 1, Seite 1-11 Online-Ressource (DE-627)NLEJ252999304 (DE-600)1476983-9 (DE-576)103868100 1532-415X nnns number:1 volume:27 year:1998 pages:1-11 https://www.tib.eu/de/suchen/id/tandf%3A8972df31f69a381755896aa406a8f3d4777ce82b Digitalisierung Deutschlandweit zugänglich ZDB-1-TFO GBV_NL_ARTICLE AR 1 27 1998 1-11
allfields_unstemmed	10.1080/03610929808832647 doi (DE-627)NLEJ253041031 (TFO)780123990 DE-627 ger DE-627 rda eng Zhang, Biao Zhang Biao verfasserin aut A note on kernel density estimation with auxiliary information 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It is well-known that the method of empirical likelihood can be employed to get sharper inferences on many functionals of the population distribution by making effective use of auxiliary information available in a nonparametric model. In this paper, we show that in the context of estimating a population density function, the modified kernel density estimator which makes use of the knowledge of auxiliary information does not sharpen our inferences on the population density function in the sense that the modified kernel density estimator is first order equivalent to the standard kernel density estimator which does not utilize auxiliary information. Enthalten in Communications in statistics / Theory and methods London : Taylor and Francis, 1982 27(1998), 1, Seite 1-11 Online-Ressource (DE-627)NLEJ252999304 (DE-600)1476983-9 (DE-576)103868100 1532-415X nnns number:1 volume:27 year:1998 pages:1-11 https://www.tib.eu/de/suchen/id/tandf%3A8972df31f69a381755896aa406a8f3d4777ce82b Digitalisierung Deutschlandweit zugänglich ZDB-1-TFO GBV_NL_ARTICLE AR 1 27 1998 1-11
allfieldsGer	10.1080/03610929808832647 doi (DE-627)NLEJ253041031 (TFO)780123990 DE-627 ger DE-627 rda eng Zhang, Biao Zhang Biao verfasserin aut A note on kernel density estimation with auxiliary information 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It is well-known that the method of empirical likelihood can be employed to get sharper inferences on many functionals of the population distribution by making effective use of auxiliary information available in a nonparametric model. In this paper, we show that in the context of estimating a population density function, the modified kernel density estimator which makes use of the knowledge of auxiliary information does not sharpen our inferences on the population density function in the sense that the modified kernel density estimator is first order equivalent to the standard kernel density estimator which does not utilize auxiliary information. Enthalten in Communications in statistics / Theory and methods London : Taylor and Francis, 1982 27(1998), 1, Seite 1-11 Online-Ressource (DE-627)NLEJ252999304 (DE-600)1476983-9 (DE-576)103868100 1532-415X nnns number:1 volume:27 year:1998 pages:1-11 https://www.tib.eu/de/suchen/id/tandf%3A8972df31f69a381755896aa406a8f3d4777ce82b Digitalisierung Deutschlandweit zugänglich ZDB-1-TFO GBV_NL_ARTICLE AR 1 27 1998 1-11
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abstract	It is well-known that the method of empirical likelihood can be employed to get sharper inferences on many functionals of the population distribution by making effective use of auxiliary information available in a nonparametric model. In this paper, we show that in the context of estimating a population density function, the modified kernel density estimator which makes use of the knowledge of auxiliary information does not sharpen our inferences on the population density function in the sense that the modified kernel density estimator is first order equivalent to the standard kernel density estimator which does not utilize auxiliary information.
abstractGer	It is well-known that the method of empirical likelihood can be employed to get sharper inferences on many functionals of the population distribution by making effective use of auxiliary information available in a nonparametric model. In this paper, we show that in the context of estimating a population density function, the modified kernel density estimator which makes use of the knowledge of auxiliary information does not sharpen our inferences on the population density function in the sense that the modified kernel density estimator is first order equivalent to the standard kernel density estimator which does not utilize auxiliary information.
abstract_unstemmed	It is well-known that the method of empirical likelihood can be employed to get sharper inferences on many functionals of the population distribution by making effective use of auxiliary information available in a nonparametric model. In this paper, we show that in the context of estimating a population density function, the modified kernel density estimator which makes use of the knowledge of auxiliary information does not sharpen our inferences on the population density function in the sense that the modified kernel density estimator is first order equivalent to the standard kernel density estimator which does not utilize auxiliary information.
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