Wavelet-based Estimator for Derivatives of Density Function for Censored and Extended Negatively Dependent Observations

Wavelet Analysis is a branch of Harmonic Analysis and a new phenomenon of Mathematics science which offers wide range of application in Mathematics, Statistics and other fields. Wavelets analysis is finding a rapidly growing number of applications despite its young age and often replacing the conventional Fourier transform. Basically in this paper, the problem of estimating a density and its derivatives for a sample of censored random variables is considered. The purpose of this paper is to present an approach to this problem based on wavelets methods for extended negatively dependent observations. Besides, we explore its performances under the risk in Besov ball.