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...
Guardado en:
Autores principales: | , |
---|---|
Formato: | JOUR |
Materias: | |
Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_03610926_v45_n2_p413_Boente |
Aporte de: |
id |
todo:paper_03610926_v45_n2_p413_Boente |
---|---|
record_format |
dspace |
spelling |
todo:paper_03610926_v45_n2_p413_Boente2023-10-03T15:26:42Z Estimating additive models with missing responses Boente, G. Martínez, A.M. 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. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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, G. Martínez, A.M. 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. |
format |
JOUR |
author |
Boente, G. Martínez, A.M. |
author_facet |
Boente, G. Martínez, A.M. |
author_sort |
Boente, G. |
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 |
url |
http://hdl.handle.net/20.500.12110/paper_03610926_v45_n2_p413_Boente |
work_keys_str_mv |
AT boenteg estimatingadditivemodelswithmissingresponses AT martinezam estimatingadditivemodelswithmissingresponses |
_version_ |
1782025784856150016 |