摘要：We consider the problem of estimating a density and its derivatives for a sample ofmultiplicatively censored random variables. The purpose of this pap er is to presentan approach to this problem based on wavelets methods. Two di.erent estimatorsare developed: a linear based on pro jections and a nonlinear using a term-by-termselection of the estimated wavelet co e.cients. We explore their performances underthe Lp-risk with p ≥ 1 and over a wide class of functions: the Besov balls. Fast ratesof convergence are obtained. Finite sample properties of the estimation procedure arestudied on a simulated data example
关键词：density estimation; multiplicative censoring; inverse problem; wavelets; Besov bal ls;L;p;-risk
