Testing in generalized partially linear models: A robust approach
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Título: | Testing in generalized partially linear models: A robust approach |
Autor/a: | Boente Boente, Graciela Cao Abad, Ricardo José González Manteiga, Wenceslao Rodríguez, Daniela |
Centro/Departamento: | Universidade de Santiago de Compostela. Departamento de Estatística e Investigación Operativa |
Palabras chave: | Generalized partially linear models | Kernel weights | Rate of convergence | Robust testing | |
Data: | 2013 |
Editor: | Elsevier |
Cita bibliográfica: | Boente, G., Cao, R., González Manteiga, W., & Rodriguez, D. (2013). Testing in generalized partially linear models: A robust approach. Statistics & Probability Letters, 83(1), 203-212. doi: 10.1016/j.spl.2012.08.031 |
Resumo: | In this paper, we introduce a family of robust statistics which allow to decide between a parametric model and a semiparametric one. More precisely, under a generalized partially linear model, i.e., when the observations satisfy yi |(xi, ti) ∼ F (·, µi) with µi = H η(ti) + x t i β and H a known link function, we want to test H0 : η(t) = α + γ t against H1 : η is a nonlinear smooth function. A general approach which includes robust estimators based on a robustified deviance or a robustified quasi-likelihood is considered. The asymptotic behavior of the test statistic under the null hypothesis is obtained |
Versión do editor: | https://doi.org/10.1016/j.spl.2012.08.031 |
URI: | http://hdl.handle.net/10347/18530 |
DOI: | 10.1016/j.spl.2012.08.031 |
ISSN: | 0167-7152 |
Dereitos: | © 2013 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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