Average Derivative Estimation from Biased Data

Wavelet block thresholding for samples with random design: a minimax approach under the L p risk, Electronic Journal of Statistics, 1, 331-346..

On adaptive wavelet estimation of the regression function and its derivatives in an errors-in-variables model... in an

In this study, we develop two kinds of wavelet estimators for the quantile density function g : a linear one based on simple projections and a nonlinear one based on a hard

Block thresholding and sharp adaptive estimation in severely ill-posed inverse problems.. Wavelet based estimation of the derivatives of a density for m-dependent

Keywords and phrases: Nonparametric regression, Derivatives function estimation, Wavelets, Besov balls, Hard

To the best of our knowledge, f b ν h is the first adaptive wavelet estimator proposed for the density estimation from mixtures under multiplicative censoring.... The variability of

For the case where d = 0 and the blurring function is ordinary-smooth or super-smooth, [22, 23, Theorems 1 and 2] provide the upper and lower bounds of the MISE over Besov balls for

The main contributions of this result is to provide more flexibility on the choices of j 0 and j 1 (which will be crucial in our dependent framework) and to clarified the