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Nonparametric estimation in a regression model with additive and multiplicative noise

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  • Christophe Chesneau
  • Salima El Kolei
  • Junke Kou
  • Fabien Navarro

Abstract

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.

Suggested Citation

  • Christophe Chesneau & Salima El Kolei & Junke Kou & Fabien Navarro, 2019. "Nonparametric estimation in a regression model with additive and multiplicative noise," Papers 1906.07695, arXiv.org, revised Jun 2020.
    • Handle: RePEc:arx:papers:1906.07695
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    References listed on IDEAS

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    3. GIJBELS, Irène & MAMMEN, Enno & PARK, Byeong U. & SIMAR, Léopold, 1997. "On estimation of monotone and concave frontier functions," LIDAM Discussion Papers CORE 1997031, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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