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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| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
Taylor and Francis Inc.
2016
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| Sumario: | 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. |
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| ISSN: | 03610926 |
| DOI: | 10.1080/03610926.2013.815780 |