  The paper considers two-phase random design linear regression models. The
errors and the regressors are stationary long-range dependent Gaussian. The
regression parameters, the scale parameters and the change-point are estimated
using a method introduced by Rousseeuw and Yohai(1984). This is called
S-estimator and it has the property that is more robust than the classical
estimators; the outliers don't spoil the estimation results. Some asymptotic
results, including the strong consistency and the convergence rate of the
S-estimators, are proved.
