Wavelet Estimation of Copula Density via Thresholding Methods Under Censoring

This paper deals with the problem of copula density estimation when data are subjected to censoring. we use a wavelet shrinkage technique to find an adaptive estimator for this problem. In particular, a hard thresholding estimator is constructed and we prove that it enjoys powerful mean integrated squared error properties over Besov balls. Moreover, it is shown that convergence rates for the mean integrated squared error of adaptive estimator are near optimal under right censoring model. Finally, a numerical example has been considered to illustrate the performance of the estimator.