Pattern Recognition Letters
Nonparametric density estimation and clustering in astronomical sky surveys
Computational Statistics & Data Analysis
Automation and Remote Control
Full bandwidth matrix selectors for gradient kernel density estimate
Computational Statistics & Data Analysis
Density estimation with minimization of U-divergence
Machine Learning
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Progress in selection of smoothing parameters for kernel density estimation has been much slower in the multivariate than univariate setting. Within the context of multivariate density estimation attention has focused on diagonal bandwidth matrices. However, there is evidence to suggest that the use of full (or unconstrained) bandwidth matrices can be beneficial. This paper presents some results in the asymptotic analysis of data-driven selectors of full bandwidth matrices. In particular, we give relative rates of convergence for plug-in selectors and a biased cross-validation selector.