Nonparametric estimation in a regression model with additive and multiplicative noise

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:380

Article

Chesneau, Christophe¹  El Kolei, Salima²  Kou, Junke³  Navarro, Fabien² 

[1] Univ Caen, LMNO, Caen, France

[2] CREST, ENSAI, Paris, France

[3] Guilin Univ Elect Technol, Guilin, Peoples R China

关键词: Nonparametric regression;    Multiplicative regression models;    Nonparametric frontier;    Rates of convergence;    Wavelets;   

DOI  :  10.1016/j.cam.2020.112971

来源: Elsevier

【 摘 要 】

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. (C) 2020 Elsevier B.V. All rights reserved.

【 授权许可】

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