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Robust estimation in partially linear measurement error models

Banff International Research Station for Mathematical Innovation and Discovery

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.

Mathematics; Statistics

video/mp4

Author affiliation: Universidad Nacional de Buenos Aires

2016-05-18

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58158

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/