2019/06/30 by Christophe Chesneau, Salima El Kolei, Junke Kou +1 · 14 citations
Economics, Econometrics and Finance · Mathematics · #Additive model #Applied mathematics #Artificial intelligence #Boundary (topology) #Computer science #Context (archaeology) #Estimator #Financial Risk and Volatility Modeling #Mathematical analysis #Mathematics #Mean squared error #Multiplicative function #Multiplicative noise #Noise (video) #Nonparametric regression #Nonparametric statistics #Statistical Methods and Inference #Statistics #Stochastic processes and financial applications #Wavelet #econ.EM #math.ST #stat.ME #stat.TH
paper · pdf · doi:10.1016/j.cam.2020.112971
published in Journal of Computational and Applied Mathematics 380, 112971 (Elsevier BV)
openalex publication_date 2020/05/11 · arxiv created 2020/06/20 · arxiv updated 2020/12/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.