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Robust Estimation and Wavelet Thresholding in Partially Linear Models

Irène Gannaz 1 
1 SMS - Statistique et Modélisation Stochatisque
LJK - Laboratoire Jean Kuntzmann
Abstract : This paper is concerned with a semiparametric partially linear regression model with unknown regression coefficients, an unknown nonparametric function for the non-linear component, and unobservable Gaussian distributed random errors. We present a wavelet thresholding based estimation procedure to estimate the components of the partial linear model by establishing a connection between an $l_1$-penalty based wavelet estimator of the nonparametric component and Huber's M-estimation of a standard linear model with outliers. Some general results on the large sample properties of the estimates of both the parametric and the nonparametric part of the model are established. Simulations and a real example are used to illustrate the general results and to compare the proposed methodology with other methods available in the recent literature.
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Submitted on : Monday, December 4, 2006 - 3:30:10 PM
Last modification on : Tuesday, October 19, 2021 - 11:13:11 PM
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Irène Gannaz. Robust Estimation and Wavelet Thresholding in Partially Linear Models. Statistics and Computing, Springer Verlag (Germany), 2007, 17 (4), pp.293-310. ⟨10.1007/s11222-007-9019-x⟩. ⟨hal-00118237⟩



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