Generalized least squares cross-validation in kernel density estimation

Jin Zhang

Statistica Neerlandica, 2015, vol. 69, issue 3, 315-328

Abstract: type="main" xml:id="stan12061-abs-0001"> The kernel density estimation is a popular method in density estimation. The main issue is bandwidth selection, which is a well-known topic and is still frustrating statisticians. A robust least squares cross-validation bandwidth is proposed, which significantly improves the classical least squares cross-validation bandwidth for its variability and undersmoothing, adapts to different kinds of densities, and outperforms the existing bandwidths in statistical literature and software.

Date: 2015
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