Estimation in semiparametric models using an auxiliary model

Abstract Two classes of semiparametric and nonparametric mixture models are defined to represent general kinds of prior information. For these models the nonparametric maximum likelihood estimator (NPMLE) of an unknown probability distribution is derived and is shown to be consistent and relative ef...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Huschens, Stefan [verfasserIn] Stahl, Gerhard

Format:	Artikel
Sprache:	Englisch

Erschienen:	1995

Schlagwörter:	semiparametric model nonparametric maximum likelihood estimation auxiliary model

Anmerkung:	© Springer-Verlag 1995

Übergeordnetes Werk:	Enthalten in: Statistical papers - Springer Berlin Heidelberg, 1988, 36(1995), 1 vom: 01. Dez., Seite 313-326
Übergeordnetes Werk:	volume:36 ; year:1995 ; number:1 ; day:01 ; month:12 ; pages:313-326

Links:	Volltext

DOI / URN:	10.1007/BF02926045

Katalog-ID:	OLC2025015453

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Indexfelder

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allfields	10.1007/BF02926045 doi (DE-627)OLC2025015453 (DE-He213)BF02926045-p DE-627 ger DE-627 rakwb eng 300 330 510 VZ Huschens, Stefan verfasserin aut Estimation in semiparametric models using an auxiliary model 1995 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1995 Abstract Two classes of semiparametric and nonparametric mixture models are defined to represent general kinds of prior information. For these models the nonparametric maximum likelihood estimator (NPMLE) of an unknown probability distribution is derived and is shown to be consistent and relative efficient. Linear functionals are used for the estimation of parameters. Their consistency is proved, the gain of efficiency is derived and asymptotical distributions are given. semiparametric model nonparametric maximum likelihood estimation auxiliary model Stahl, Gerhard aut Enthalten in Statistical papers Springer Berlin Heidelberg, 1988 36(1995), 1 vom: 01. Dez., Seite 313-326 (DE-627)129572292 (DE-600)227641-0 (DE-576)015069486 0932-5026 nnns volume:36 year:1995 number:1 day:01 month:12 pages:313-326 https://doi.org/10.1007/BF02926045 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_65 GBV_ILN_70 GBV_ILN_120 GBV_ILN_151 GBV_ILN_754 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4309 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4319 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4700 AR 36 1995 1 01 12 313-326
spelling	10.1007/BF02926045 doi (DE-627)OLC2025015453 (DE-He213)BF02926045-p DE-627 ger DE-627 rakwb eng 300 330 510 VZ Huschens, Stefan verfasserin aut Estimation in semiparametric models using an auxiliary model 1995 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1995 Abstract Two classes of semiparametric and nonparametric mixture models are defined to represent general kinds of prior information. For these models the nonparametric maximum likelihood estimator (NPMLE) of an unknown probability distribution is derived and is shown to be consistent and relative efficient. Linear functionals are used for the estimation of parameters. Their consistency is proved, the gain of efficiency is derived and asymptotical distributions are given. semiparametric model nonparametric maximum likelihood estimation auxiliary model Stahl, Gerhard aut Enthalten in Statistical papers Springer Berlin Heidelberg, 1988 36(1995), 1 vom: 01. Dez., Seite 313-326 (DE-627)129572292 (DE-600)227641-0 (DE-576)015069486 0932-5026 nnns volume:36 year:1995 number:1 day:01 month:12 pages:313-326 https://doi.org/10.1007/BF02926045 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_65 GBV_ILN_70 GBV_ILN_120 GBV_ILN_151 GBV_ILN_754 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4309 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4319 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4700 AR 36 1995 1 01 12 313-326
allfields_unstemmed	10.1007/BF02926045 doi (DE-627)OLC2025015453 (DE-He213)BF02926045-p DE-627 ger DE-627 rakwb eng 300 330 510 VZ Huschens, Stefan verfasserin aut Estimation in semiparametric models using an auxiliary model 1995 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1995 Abstract Two classes of semiparametric and nonparametric mixture models are defined to represent general kinds of prior information. For these models the nonparametric maximum likelihood estimator (NPMLE) of an unknown probability distribution is derived and is shown to be consistent and relative efficient. Linear functionals are used for the estimation of parameters. Their consistency is proved, the gain of efficiency is derived and asymptotical distributions are given. semiparametric model nonparametric maximum likelihood estimation auxiliary model Stahl, Gerhard aut Enthalten in Statistical papers Springer Berlin Heidelberg, 1988 36(1995), 1 vom: 01. Dez., Seite 313-326 (DE-627)129572292 (DE-600)227641-0 (DE-576)015069486 0932-5026 nnns volume:36 year:1995 number:1 day:01 month:12 pages:313-326 https://doi.org/10.1007/BF02926045 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_65 GBV_ILN_70 GBV_ILN_120 GBV_ILN_151 GBV_ILN_754 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4309 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4319 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4700 AR 36 1995 1 01 12 313-326
allfieldsGer	10.1007/BF02926045 doi (DE-627)OLC2025015453 (DE-He213)BF02926045-p DE-627 ger DE-627 rakwb eng 300 330 510 VZ Huschens, Stefan verfasserin aut Estimation in semiparametric models using an auxiliary model 1995 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1995 Abstract Two classes of semiparametric and nonparametric mixture models are defined to represent general kinds of prior information. For these models the nonparametric maximum likelihood estimator (NPMLE) of an unknown probability distribution is derived and is shown to be consistent and relative efficient. Linear functionals are used for the estimation of parameters. Their consistency is proved, the gain of efficiency is derived and asymptotical distributions are given. semiparametric model nonparametric maximum likelihood estimation auxiliary model Stahl, Gerhard aut Enthalten in Statistical papers Springer Berlin Heidelberg, 1988 36(1995), 1 vom: 01. Dez., Seite 313-326 (DE-627)129572292 (DE-600)227641-0 (DE-576)015069486 0932-5026 nnns volume:36 year:1995 number:1 day:01 month:12 pages:313-326 https://doi.org/10.1007/BF02926045 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_65 GBV_ILN_70 GBV_ILN_120 GBV_ILN_151 GBV_ILN_754 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4309 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4319 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4700 AR 36 1995 1 01 12 313-326
allfieldsSound	10.1007/BF02926045 doi (DE-627)OLC2025015453 (DE-He213)BF02926045-p DE-627 ger DE-627 rakwb eng 300 330 510 VZ Huschens, Stefan verfasserin aut Estimation in semiparametric models using an auxiliary model 1995 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1995 Abstract Two classes of semiparametric and nonparametric mixture models are defined to represent general kinds of prior information. For these models the nonparametric maximum likelihood estimator (NPMLE) of an unknown probability distribution is derived and is shown to be consistent and relative efficient. Linear functionals are used for the estimation of parameters. Their consistency is proved, the gain of efficiency is derived and asymptotical distributions are given. semiparametric model nonparametric maximum likelihood estimation auxiliary model Stahl, Gerhard aut Enthalten in Statistical papers Springer Berlin Heidelberg, 1988 36(1995), 1 vom: 01. Dez., Seite 313-326 (DE-627)129572292 (DE-600)227641-0 (DE-576)015069486 0932-5026 nnns volume:36 year:1995 number:1 day:01 month:12 pages:313-326 https://doi.org/10.1007/BF02926045 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_65 GBV_ILN_70 GBV_ILN_120 GBV_ILN_151 GBV_ILN_754 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4193 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4309 GBV_ILN_4310 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4319 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4700 AR 36 1995 1 01 12 313-326
language	English
source	Enthalten in Statistical papers 36(1995), 1 vom: 01. Dez., Seite 313-326 volume:36 year:1995 number:1 day:01 month:12 pages:313-326
sourceStr	Enthalten in Statistical papers 36(1995), 1 vom: 01. Dez., Seite 313-326 volume:36 year:1995 number:1 day:01 month:12 pages:313-326
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	semiparametric model nonparametric maximum likelihood estimation auxiliary model
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authorswithroles_txt_mv	Huschens, Stefan @@aut@@ Stahl, Gerhard @@aut@@
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author	Huschens, Stefan
spellingShingle	Huschens, Stefan ddc 300 misc semiparametric model misc nonparametric maximum likelihood estimation misc auxiliary model Estimation in semiparametric models using an auxiliary model
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topic_title	300 330 510 VZ Estimation in semiparametric models using an auxiliary model semiparametric model nonparametric maximum likelihood estimation auxiliary model
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title	Estimation in semiparametric models using an auxiliary model
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title_full	Estimation in semiparametric models using an auxiliary model
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title_sort	estimation in semiparametric models using an auxiliary model
title_auth	Estimation in semiparametric models using an auxiliary model
abstract	Abstract Two classes of semiparametric and nonparametric mixture models are defined to represent general kinds of prior information. For these models the nonparametric maximum likelihood estimator (NPMLE) of an unknown probability distribution is derived and is shown to be consistent and relative efficient. Linear functionals are used for the estimation of parameters. Their consistency is proved, the gain of efficiency is derived and asymptotical distributions are given. © Springer-Verlag 1995
abstractGer	Abstract Two classes of semiparametric and nonparametric mixture models are defined to represent general kinds of prior information. For these models the nonparametric maximum likelihood estimator (NPMLE) of an unknown probability distribution is derived and is shown to be consistent and relative efficient. Linear functionals are used for the estimation of parameters. Their consistency is proved, the gain of efficiency is derived and asymptotical distributions are given. © Springer-Verlag 1995
abstract_unstemmed	Abstract Two classes of semiparametric and nonparametric mixture models are defined to represent general kinds of prior information. For these models the nonparametric maximum likelihood estimator (NPMLE) of an unknown probability distribution is derived and is shown to be consistent and relative efficient. Linear functionals are used for the estimation of parameters. Their consistency is proved, the gain of efficiency is derived and asymptotical distributions are given. © Springer-Verlag 1995
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title_short	Estimation in semiparametric models using an auxiliary model
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