Kernel adjusted density estimation

We propose and study a kernel estimator of a density in which the kernel is adapted to the data but not fixed. The smoothing procedure is followed by a location-scale transformation to reduce bias and variance. The new method naturally leads to an adaptive choice of the smoothing parameters which avoids asymptotic expansions. (C) 2011 Elsevier B.V. All rights reserved.