Additive nonparametric regression estimation via back tting and marginal integration under common bandwidth selection criterion : small sample performance
Autor(a) principal: | |
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Data de Publicação: | 2006 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/109669 |
Resumo: | In this paper, we conducted a Monte Carlo investigation to reveal some charac- teristics of nite sample distributions of the back tting (B) and Marginal Integration (MI) estimators for an additive bivariate regression. We are particularly interested in providing some evidence on how the di¤erent methods for the selection of bandwidth, such as the plug-in method, inuence the nite sample properties of the MI and B estimators. We are particularly concerned with the performance of these estimators when bandwidth selection is done based in data driven methods, since in this case the aymptotics properties of these estimators are currently unavailable. The impact of ignoring the dependency between regressors is also investigated. Finally, di¤erently from what occurs at the present time, when the B and MI estimators are used ad-hoc, our objective is to provide information that allows for a more accurate comparison of these two competing alternatives in a nite sample setting. |
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Silva, Fernando Augusto Boeira Sabino daSen, Pranab Kumar2015-02-05T02:17:13Z2006http://hdl.handle.net/10183/109669000951323In this paper, we conducted a Monte Carlo investigation to reveal some charac- teristics of
nite sample distributions of the back
tting (B) and Marginal Integration (MI) estimators for an additive bivariate regression. We are particularly interested in providing some evidence on how the di¤erent methods for the selection of bandwidth, such as the plug-in method, inuence the
nite sample properties of the MI and B estimators. We are particularly concerned with the performance of these estimators when bandwidth selection is done based in data driven methods, since in this case the aymptotics properties of these estimators are currently unavailable. The impact of ignoring the dependency between regressors is also investigated. Finally, di¤erently from what occurs at the present time, when the B and MI estimators are used ad-hoc, our objective is to provide information that allows for a more accurate comparison of these two competing alternatives in a
nite sample setting.application/pdfporEconomiaMétodos qualitativosModelos matemáticosEconometriaAdditive nonparametric regression estimation via back
tting and marginal integration under common bandwidth selection criterion : small sample performanceinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisUniversity of North Carolina at Chapel HillDepartment of Statistics & Operations ResearchChapel Hill, Carolina do Norte - USA2006mestradoinfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL000951323.pdf000951323.pdfTexto completoapplication/pdf263118http://www.lume.ufrgs.br/bitstream/10183/109669/1/000951323.pdf6519ae52a224338b35fc5e9c3deac1d6MD51TEXT000951323.pdf.txt000951323.pdf.txtExtracted Texttext/plain39601http://www.lume.ufrgs.br/bitstream/10183/109669/2/000951323.pdf.txtec40b6bfb9e3659c2b76c01cd3081666MD52THUMBNAIL000951323.pdf.jpg000951323.pdf.jpgGenerated Thumbnailimage/jpeg1382http://www.lume.ufrgs.br/bitstream/10183/109669/3/000951323.pdf.jpg8817b2f3592a778d921f8f51b0d0cefcMD5310183/1096692018-10-23 09:08:16.029oai:www.lume.ufrgs.br:10183/109669Biblioteca Digital de Teses e Dissertaçõeshttps://lume.ufrgs.br/handle/10183/2PUBhttps://lume.ufrgs.br/oai/requestlume@ufrgs.br||lume@ufrgs.bropendoar:18532018-10-23T12:08:16Biblioteca Digital de Teses e Dissertações da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Additive nonparametric regression estimation via back
tting and marginal integration under common bandwidth selection criterion : small sample performance |
title |
Additive nonparametric regression estimation via back
tting and marginal integration under common bandwidth selection criterion : small sample performance |
spellingShingle |
Additive nonparametric regression estimation via back
tting and marginal integration under common bandwidth selection criterion : small sample performance Silva, Fernando Augusto Boeira Sabino da Economia Métodos qualitativos Modelos matemáticos Econometria |
title_short |
Additive nonparametric regression estimation via back
tting and marginal integration under common bandwidth selection criterion : small sample performance |
title_full |
Additive nonparametric regression estimation via back
tting and marginal integration under common bandwidth selection criterion : small sample performance |
title_fullStr |
Additive nonparametric regression estimation via back
tting and marginal integration under common bandwidth selection criterion : small sample performance |
title_full_unstemmed |
Additive nonparametric regression estimation via back
tting and marginal integration under common bandwidth selection criterion : small sample performance |
title_sort |
Additive nonparametric regression estimation via back
tting and marginal integration under common bandwidth selection criterion : small sample performance |
author |
Silva, Fernando Augusto Boeira Sabino da |
author_facet |
Silva, Fernando Augusto Boeira Sabino da |
author_role |
author |
dc.contributor.author.fl_str_mv |
Silva, Fernando Augusto Boeira Sabino da |
dc.contributor.advisor1.fl_str_mv |
Sen, Pranab Kumar |
contributor_str_mv |
Sen, Pranab Kumar |
dc.subject.por.fl_str_mv |
Economia Métodos qualitativos Modelos matemáticos Econometria |
topic |
Economia Métodos qualitativos Modelos matemáticos Econometria |
description |
In this paper, we conducted a Monte Carlo investigation to reveal some charac- teristics of
nite sample distributions of the back
tting (B) and Marginal Integration (MI) estimators for an additive bivariate regression. We are particularly interested in providing some evidence on how the di¤erent methods for the selection of bandwidth, such as the plug-in method, inuence the
nite sample properties of the MI and B estimators. We are particularly concerned with the performance of these estimators when bandwidth selection is done based in data driven methods, since in this case the aymptotics properties of these estimators are currently unavailable. The impact of ignoring the dependency between regressors is also investigated. Finally, di¤erently from what occurs at the present time, when the B and MI estimators are used ad-hoc, our objective is to provide information that allows for a more accurate comparison of these two competing alternatives in a
nite sample setting. |
publishDate |
2006 |
dc.date.issued.fl_str_mv |
2006 |
dc.date.accessioned.fl_str_mv |
2015-02-05T02:17:13Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/109669 |
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000951323 |
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identifier_str_mv |
000951323 |
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por |
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openAccess |
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