We consider wavelet based method for estimating derivatives of a density via block thresholding when the data obtained are randomly right censored. The proposed method is analogous to that of Hall and Patil (1995) for density estimation in the complete data case that has been extended recently by Li (2003, 2008). We find bounds for the L 2-loss over a large range of Besov function classes for the resulting estimators. The results of Hall and Patil (1995), Prakasa Rao (1996) and Li (2003, 2008) are obtained as special cases and the performance of the proposed estimator is investigated by a numerical study.
- Adaptive estimation
- Block thresholding
- Censored data
- Nonparametric estimator of derivative of a density
- Rates of convergence