Abstract
AbstractThis paper is concerned with the problem of estimating the convolution of densities. We propose an adaptive estimator based on kernel methods, Fourier analysis and the Lepski method. We study the ‐risk properties of the estimator. Fast and new rates of convergence are determined for a wide class of unknown functions. Numerical illustrations, on both simulated and real data, are provided to assess the performance of our procedure. The Canadian Journal of Statistics 41: 617–636; 2013 © 2013 Statistical Society of Canada
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