Kernel adjusted density estimation

Ramidha Srihera and Winfried Stute

Statistics & Probability Letters, 2011, vol. 81, issue 5, 571-579

Abstract: 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.

Keywords: Kernel; density; estimator; Adaptive; choice (search for similar items in EconPapers)
Date: 2011
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