Nonparametric estimation in a regression model with additive and multiplicative noise
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In this paper, we consider an unknown functional estimation problem in a general nonparametric regression model with the characteristic of having both multiplicative and additive noise. We propose two wavelet estimators, which, to our knowledge, are new in this general context. We prove that they achieve fast convergence rates under the mean integrated square error over Besov spaces. The rates obtained 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.
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