Estimating additive models with missing responses

For multivariate regressors, the Nadaraya-Watson regression estimator suffers from the well-known curse of dimensionality. Additive models overcome this drawback. To estimate the additive components, it is usually assumed that we observe all the data. However, in many applied statistical analysis mi...

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Detalles Bibliográficos
Autores principales: Boente, Graciela Lina, Martinez, Alejandra Mercedes
Publicado: 2016
Materias:
Additive models
Kernel weights
Marginal integration
Missing Data
Non parametric regression
Statistical methods
Statistics
Kernel weight
Missing data
Non-parametric regression
Estimation
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03610926_v45_n2_p413_Boente
http://hdl.handle.net/20.500.12110/paper_03610926_v45_n2_p413_Boente
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_03610926_v45_n2_p413_Boente
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spelling paper:paper_03610926_v45_n2_p413_Boente2023-06-08T15:34:54Z Estimating additive models with missing responses Boente, Graciela Lina Martinez, Alejandra Mercedes Additive models Kernel weights Marginal integration Missing Data Non parametric regression Statistical methods Statistics Additive models Kernel weight Marginal integration Missing data Non-parametric regression Estimation For multivariate regressors, the Nadaraya-Watson regression estimator suffers from the well-known curse of dimensionality. Additive models overcome this drawback. To estimate the additive components, it is usually assumed that we observe all the data. However, in many applied statistical analysis missing data occur. In this paper, we study the effect of missing responses on the additive components estimation. The estimators are based on marginal integration adapted to the missing situation. The proposed estimators turn out to be consistent under mild assumptions. A simulation study allows to compare the behavior of our procedures, under different scenarios. © 2016 Taylor & Francis Group, LLC. Fil:Boente, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Martínez, A.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03610926_v45_n2_p413_Boente http://hdl.handle.net/20.500.12110/paper_03610926_v45_n2_p413_Boente
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Additive models
Kernel weights
Marginal integration
Missing Data
Non parametric regression
Statistical methods
Statistics
Additive models
Kernel weight
Marginal integration
Missing data
Non-parametric regression
Estimation
spellingShingle Additive models
Kernel weights
Marginal integration
Missing Data
Non parametric regression
Statistical methods
Statistics
Additive models
Kernel weight
Marginal integration
Missing data
Non-parametric regression
Estimation
Boente, Graciela Lina
Martinez, Alejandra Mercedes
Estimating additive models with missing responses
topic_facet Additive models
Kernel weights
Marginal integration
Missing Data
Non parametric regression
Statistical methods
Statistics
Additive models
Kernel weight
Marginal integration
Missing data
Non-parametric regression
Estimation
description For multivariate regressors, the Nadaraya-Watson regression estimator suffers from the well-known curse of dimensionality. Additive models overcome this drawback. To estimate the additive components, it is usually assumed that we observe all the data. However, in many applied statistical analysis missing data occur. In this paper, we study the effect of missing responses on the additive components estimation. The estimators are based on marginal integration adapted to the missing situation. The proposed estimators turn out to be consistent under mild assumptions. A simulation study allows to compare the behavior of our procedures, under different scenarios. © 2016 Taylor & Francis Group, LLC.
author Boente, Graciela Lina
Martinez, Alejandra Mercedes
author_facet Boente, Graciela Lina
Martinez, Alejandra Mercedes
author_sort Boente, Graciela Lina
title Estimating additive models with missing responses
title_short Estimating additive models with missing responses
title_full Estimating additive models with missing responses
title_fullStr Estimating additive models with missing responses
title_full_unstemmed Estimating additive models with missing responses
title_sort estimating additive models with missing responses
publishDate 2016
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03610926_v45_n2_p413_Boente
http://hdl.handle.net/20.500.12110/paper_03610926_v45_n2_p413_Boente
work_keys_str_mv AT boentegracielalina estimatingadditivemodelswithmissingresponses
AT martinezalejandramercedes estimatingadditivemodelswithmissingresponses
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