We consider the nonparametric robust estimation problem for regression models
in continuous time with semi-Markov noises. An adaptive model selection
procedure is proposed. Under general moment conditions on the noise
distribution a sharp non-asymptotic oracle inequality for the robust risks is
obtained and the robust efficiency is shown. It turns out that for semi-Markov
models the robust minimax convergence rate may be faster or slower than the
classical one
