Wavelet estimators for the derivatives of the density function from data contaminated with heteroscedastic measurement errors

Jinru Wang, Qingqing Zhang and Junke Kou

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 15, 7337-7354

Abstract: Measurement errors occur in many real data applications. In this paper, the linear and the non linear wavelet estimators of the derivatives of the density function are constructed in the case of data contaminated with heteroscedastic measurement errors. We establish Lp risk performance of the estimators and show that they achieve fast convergence rates under quite general conditions.

Date: 2017
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DOI: 10.1080/03610926.2016.1148735

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