  We consider in this paper a contamined regression model where the
distribution of the contaminating component is known when the Eu- clidean
parameters of the regression model, the noise distribution, the contamination
ratio and the distribution of the design data are un- known. Our model is said
to be semiparametric in the sense that the probability density function (pdf)
of the noise involved in the regression model is not supposed to belong to a
parametric density family. When the pdf's of the noise and the contaminating
phenomenon are supposed to be symmetric about zero, we propose an estimator of
the various (Eu- clidean and functionnal) parameters of the model, and prove
under mild conditions its convergence. We prove in particular that, under
technical conditions all satisfied in the Gaussian case, the Euclidean part of
the model is estimated at the rate $o_{a.s}(n-1/4+\gamma), $\gamma> 0$. We
recall that, as it is pointed out in Bordes and Vandekerkhove (2010), this
result cannot be ignored to go further in the asymptotic theory for this class
of models. Finally the implementation and numerical performances of our method
are discussed on several toy examples.
