Linear wavelet estimation of the derivatives of a regression function based on biased data

Yogendra P. Chaubey, Christophe Chesneau and Fabien Navarro

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 19, 9541-9556

Abstract: This article deals with the problem of estimating the derivatives of a regression function based on biased data. We develop two different linear wavelet estimators according to the knowledge of the “biased density” of the design. The new estimators are analyzed with respect to their Lp$\mathbb {L}^p$-risk with p ⩾ 1 over Besov balls. Fast polynomial rates of convergence are obtained.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:46:y:2017:i:19:p:9541-9556

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DOI: 10.1080/03610926.2016.1213287

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