Generalized least squares cross‐validation in kernel density estimation

descriptionPublicationkeyboard_double_arrow_right Article 25 Feb 2015 English Publisher:WileyJournal:Statistica Neerlandica, volume 69, pages 315-328 (issn: 0039-0402, eissn: 1467-9574,

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Authors: Zhang, Jin;

doi: 10.1111/stan.12061

Generalized least squares cross‐validation in kernel density estimation

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Abstract

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.

Related Organizations

Yunnan Open University
China (People's Republic of)

Keywords

bandwidth, Density estimation, integrated squared error, normal mixture, oversmoothing, undersmoothing

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