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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… Click to show full abstract
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.
Keywords:
estimators derivatives;
measurement errors;
data contaminated;
density function;
derivatives density;
wavelet estimators
Journal Title: Communications in Statistics - Theory and Methods
Year Published: 2017
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Journal Title: Communications in Statistics - Theory and Methods
Year Published: 2017
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!