Parametric and nonparametric frequentist model selection and model averaging

This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, est...
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Autor*in:	Ullah, Aman - 1946- [verfasserIn] Wang, Huansha [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Beschreibung:	Systemvoraussetzung: Acrobat Reader.

Übergeordnetes Werk:	In: Econometrics - Basel : MDPI, 2013, 1(2013), 2 vom: Sept., Seite 157-179
Übergeordnetes Werk:	volume:1 ; year:2013 ; number:2 ; month:09 ; pages:157-179

Links:	Volltext Volltext

DOI / URN:	10.3390/econometrics1020157

Katalog-ID:	77752063X

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allfields	10.3390/econometrics1020157 doi 10419/103630 hdl (DE-627)77752063X (DE-599)GBV77752063X DE-627 ger DE-627 rakwb eng Ullah, Aman 1946- verfasserin (DE-588)112170048 (DE-627)521425956 (DE-576)289723175 aut Parametric and nonparametric frequentist model selection and model averaging Aman Ullah and Huansha Wang 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, estimation and inference are often conducted under the selected model without considering the uncertainty from the selection process. This often leads to inefficiency in results and misleading confidence intervals. Thus an alternative to model selection is model averaging where the estimated model is the weighted sum of all the submodels. This reduces model uncertainty. In recent years, there has been significant interest in model averaging and some important developments have taken place in this area. We present results for both the parametric and nonparametric cases. Some possible topics for future research are also indicated. Systemvoraussetzung: Acrobat Reader. Wang, Huansha verfasserin aut In Econometrics Basel : MDPI, 2013 1(2013), 2 vom: Sept., Seite 157-179 Online-Ressource (DE-627)74684042X (DE-600)2717594-7 (DE-576)382897196 2225-1146 nnns volume:1 year:2013 number:2 month:09 pages:157-179 http://dx.doi.org/10.3390/econometrics1020157 Resolving-System Volltext http://hdl.handle.net/10419/103630 Download aus dem Internet, Stand: 03.02.2014 Volltext GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 zbwolc20140214 AR 1 2013 2 9 157-179 26 01 0206 1455783099 z1k 03-02-14 26 00 DE-206 56 nonparametric 26 00 DE-206 56 model selection 26 00 DE-206 56 model averaging
spelling	10.3390/econometrics1020157 doi 10419/103630 hdl (DE-627)77752063X (DE-599)GBV77752063X DE-627 ger DE-627 rakwb eng Ullah, Aman 1946- verfasserin (DE-588)112170048 (DE-627)521425956 (DE-576)289723175 aut Parametric and nonparametric frequentist model selection and model averaging Aman Ullah and Huansha Wang 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, estimation and inference are often conducted under the selected model without considering the uncertainty from the selection process. This often leads to inefficiency in results and misleading confidence intervals. Thus an alternative to model selection is model averaging where the estimated model is the weighted sum of all the submodels. This reduces model uncertainty. In recent years, there has been significant interest in model averaging and some important developments have taken place in this area. We present results for both the parametric and nonparametric cases. Some possible topics for future research are also indicated. Systemvoraussetzung: Acrobat Reader. Wang, Huansha verfasserin aut In Econometrics Basel : MDPI, 2013 1(2013), 2 vom: Sept., Seite 157-179 Online-Ressource (DE-627)74684042X (DE-600)2717594-7 (DE-576)382897196 2225-1146 nnns volume:1 year:2013 number:2 month:09 pages:157-179 http://dx.doi.org/10.3390/econometrics1020157 Resolving-System Volltext http://hdl.handle.net/10419/103630 Download aus dem Internet, Stand: 03.02.2014 Volltext GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 zbwolc20140214 AR 1 2013 2 9 157-179 26 01 0206 1455783099 z1k 03-02-14 26 00 DE-206 56 nonparametric 26 00 DE-206 56 model selection 26 00 DE-206 56 model averaging
allfields_unstemmed	10.3390/econometrics1020157 doi 10419/103630 hdl (DE-627)77752063X (DE-599)GBV77752063X DE-627 ger DE-627 rakwb eng Ullah, Aman 1946- verfasserin (DE-588)112170048 (DE-627)521425956 (DE-576)289723175 aut Parametric and nonparametric frequentist model selection and model averaging Aman Ullah and Huansha Wang 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, estimation and inference are often conducted under the selected model without considering the uncertainty from the selection process. This often leads to inefficiency in results and misleading confidence intervals. Thus an alternative to model selection is model averaging where the estimated model is the weighted sum of all the submodels. This reduces model uncertainty. In recent years, there has been significant interest in model averaging and some important developments have taken place in this area. We present results for both the parametric and nonparametric cases. Some possible topics for future research are also indicated. Systemvoraussetzung: Acrobat Reader. Wang, Huansha verfasserin aut In Econometrics Basel : MDPI, 2013 1(2013), 2 vom: Sept., Seite 157-179 Online-Ressource (DE-627)74684042X (DE-600)2717594-7 (DE-576)382897196 2225-1146 nnns volume:1 year:2013 number:2 month:09 pages:157-179 http://dx.doi.org/10.3390/econometrics1020157 Resolving-System Volltext http://hdl.handle.net/10419/103630 Download aus dem Internet, Stand: 03.02.2014 Volltext GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 zbwolc20140214 AR 1 2013 2 9 157-179 26 01 0206 1455783099 z1k 03-02-14 26 00 DE-206 56 nonparametric 26 00 DE-206 56 model selection 26 00 DE-206 56 model averaging
allfieldsGer	10.3390/econometrics1020157 doi 10419/103630 hdl (DE-627)77752063X (DE-599)GBV77752063X DE-627 ger DE-627 rakwb eng Ullah, Aman 1946- verfasserin (DE-588)112170048 (DE-627)521425956 (DE-576)289723175 aut Parametric and nonparametric frequentist model selection and model averaging Aman Ullah and Huansha Wang 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, estimation and inference are often conducted under the selected model without considering the uncertainty from the selection process. This often leads to inefficiency in results and misleading confidence intervals. Thus an alternative to model selection is model averaging where the estimated model is the weighted sum of all the submodels. This reduces model uncertainty. In recent years, there has been significant interest in model averaging and some important developments have taken place in this area. We present results for both the parametric and nonparametric cases. Some possible topics for future research are also indicated. Systemvoraussetzung: Acrobat Reader. Wang, Huansha verfasserin aut In Econometrics Basel : MDPI, 2013 1(2013), 2 vom: Sept., Seite 157-179 Online-Ressource (DE-627)74684042X (DE-600)2717594-7 (DE-576)382897196 2225-1146 nnns volume:1 year:2013 number:2 month:09 pages:157-179 http://dx.doi.org/10.3390/econometrics1020157 Resolving-System Volltext http://hdl.handle.net/10419/103630 Download aus dem Internet, Stand: 03.02.2014 Volltext GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 zbwolc20140214 AR 1 2013 2 9 157-179 26 01 0206 1455783099 z1k 03-02-14 26 00 DE-206 56 nonparametric 26 00 DE-206 56 model selection 26 00 DE-206 56 model averaging
allfieldsSound	10.3390/econometrics1020157 doi 10419/103630 hdl (DE-627)77752063X (DE-599)GBV77752063X DE-627 ger DE-627 rakwb eng Ullah, Aman 1946- verfasserin (DE-588)112170048 (DE-627)521425956 (DE-576)289723175 aut Parametric and nonparametric frequentist model selection and model averaging Aman Ullah and Huansha Wang 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, estimation and inference are often conducted under the selected model without considering the uncertainty from the selection process. This often leads to inefficiency in results and misleading confidence intervals. Thus an alternative to model selection is model averaging where the estimated model is the weighted sum of all the submodels. This reduces model uncertainty. In recent years, there has been significant interest in model averaging and some important developments have taken place in this area. We present results for both the parametric and nonparametric cases. Some possible topics for future research are also indicated. Systemvoraussetzung: Acrobat Reader. Wang, Huansha verfasserin aut In Econometrics Basel : MDPI, 2013 1(2013), 2 vom: Sept., Seite 157-179 Online-Ressource (DE-627)74684042X (DE-600)2717594-7 (DE-576)382897196 2225-1146 nnns volume:1 year:2013 number:2 month:09 pages:157-179 http://dx.doi.org/10.3390/econometrics1020157 Resolving-System Volltext http://hdl.handle.net/10419/103630 Download aus dem Internet, Stand: 03.02.2014 Volltext GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 zbwolc20140214 AR 1 2013 2 9 157-179 26 01 0206 1455783099 z1k 03-02-14 26 00 DE-206 56 nonparametric 26 00 DE-206 56 model selection 26 00 DE-206 56 model averaging
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author	Ullah, Aman 1946-
spellingShingle	Ullah, Aman 1946- 26 nonparametric 26 model selection 26 model averaging Parametric and nonparametric frequentist model selection and model averaging
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topic_title	26 00 DE-206 56 nonparametric 26 00 DE-206 56 model selection 26 00 DE-206 56 model averaging Parametric and nonparametric frequentist model selection and model averaging Aman Ullah and Huansha Wang
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title_full	Parametric and nonparametric frequentist model selection and model averaging Aman Ullah and Huansha Wang
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title_sort	parametric and nonparametric frequentist model selection and model averaging
title_auth	Parametric and nonparametric frequentist model selection and model averaging
abstract	This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, estimation and inference are often conducted under the selected model without considering the uncertainty from the selection process. This often leads to inefficiency in results and misleading confidence intervals. Thus an alternative to model selection is model averaging where the estimated model is the weighted sum of all the submodels. This reduces model uncertainty. In recent years, there has been significant interest in model averaging and some important developments have taken place in this area. We present results for both the parametric and nonparametric cases. Some possible topics for future research are also indicated.
abstractGer	This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, estimation and inference are often conducted under the selected model without considering the uncertainty from the selection process. This often leads to inefficiency in results and misleading confidence intervals. Thus an alternative to model selection is model averaging where the estimated model is the weighted sum of all the submodels. This reduces model uncertainty. In recent years, there has been significant interest in model averaging and some important developments have taken place in this area. We present results for both the parametric and nonparametric cases. Some possible topics for future research are also indicated.
abstract_unstemmed	This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, estimation and inference are often conducted under the selected model without considering the uncertainty from the selection process. This often leads to inefficiency in results and misleading confidence intervals. Thus an alternative to model selection is model averaging where the estimated model is the weighted sum of all the submodels. This reduces model uncertainty. In recent years, there has been significant interest in model averaging and some important developments have taken place in this area. We present results for both the parametric and nonparametric cases. Some possible topics for future research are also indicated.
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title_short	Parametric and nonparametric frequentist model selection and model averaging
url	http://dx.doi.org/10.3390/econometrics1020157 http://hdl.handle.net/10419/103630
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Parametric and nonparametric frequentist model selection and model averaging

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