We consider the density convolution model: where X and e are independent random variables that the density of X is a finite mixture with unknown components. To estimate a component of this mixture from pairwise positive quadrant dependent observations a linear wavelet estimator is developed. Further, we measure its performance by determining an upper bound of the mean integrated squared error.
