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

Yogendra P. Chaubey (), Christophe Chesneau () and Fabien Navarro ()
Additional contact information
Yogendra P. Chaubey: Department of Mathematics and Statistics, Concordia University
Christophe Chesneau: Université de Caen; LMNO
Fabien Navarro: CREST; ENSAI

No 2017-70, Working Papers from Center for Research in Economics and Statistics

Abstract: This paper 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 risk with p>1 over Besov balls. Fast polynomial rates of convergence are obtained.

Keywords: Nonparametric regression; Biased data; Deriva-tives function estimation; Wavelets; Besov balls (search for similar items in EconPapers)
Pages: 19 pages
Date: 2017
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