  In this paper, we consider an unknown functional estimation problem in a
general nonparametric regression model with the feature of having both
multiplicative and additive noise.We propose two new wavelet estimators in this
general context. We prove that they achieve fast convergence rates under the
mean integrated square error over Besov spaces. The obtained rates have the
particularity of being established under weak conditions on the model. A
numerical study in a context comparable to stochastic frontier estimation (with
the difference that the boundary is not necessarily a production function)
supports the theory.
