A note on the wavelet deconvolution of a density from mixtures under quadrant dependence
Résumé
We consider the density convolution model: $Y=X+\epsilon$, where $X$ and $\epsilon$ are independent random variables. We suppose that the density of $X$ is a finite mixture with unknown components. We want to estimate a component of this mixture from pairwise positive quadrant dependent observations $Y_1,\ldots,Y_n$. To reach this goal, a linear wavelet estimator is developed. We measure its performance by determining an upper bound of the mean integrated squared error.
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