Robust estimation in partially linear measurement error models

In many applications of regression analysis, there are covariates that are measured with errors. Measurement error models are a useful tool for the analysis in this kind of situations. Among semipara- metric models, partially linear models have been extensively used due to their flexibility to model linear components in conjunction with non-parametric ones. In this talk, we focus on partially linear models where the covariates of the linear component are measured with additive errors. We consider a robust fam- ily of estimators of the parametric and nonparametric components that combine robust local smoothers with robust parametric techniques. The resulting estimators are based on a three-step procedure. We prove that, under regularity conditions, they are consistent. We study their robustness by means of the empirical influence function. A simulation study allows to compare the behaviour of the robust estimators with their classical relatives and a real example data is analysed to illustrate the performance of the proposal.Non UBCUnreviewedAuthor affiliation: Universidad Nacional de Buenos AiresFacult