Testing in generalized partially linear models: A robust approach
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 y i(x i,t i)F(i) with μ i = H((t i)+x i t) and H a known link function,...
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| Autores principales: | , , , |
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| Formato: | JOUR |
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_01677152_v83_n1_p203_Boente |
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| Sumario: | 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 y i(x i,t i)F(i) with μ i = H((t i)+x i t) and H a known link function, we want to test H0:(t)=+t against H1: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. © 2012 Elsevier B.V. |
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