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Fast Computation of Multivariate Kernel Estimators

M. P. Wand
Journal of Computational and Graphical Statistics
Vol. 3, No. 4 (Dec., 1994), pp. 433-445
DOI: 10.2307/1390904
Stable URL: http://www.jstor.org/stable/1390904
Page Count: 13
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Fast Computation of Multivariate Kernel Estimators
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Abstract

Multivariate extensions of binning techniques for fast computation of kernel estimators are described and examined. Several questions arising from this multivariate extension are addressed. The choice of binning rule is discussed, and it is demonstrated that linear binning leads to substantial accuracy improvements over simple binning. An investigation into the most appropriate means of computing the multivariate discrete convolutions required for binned kernel estimators is also given. The results of an empirical study indicate that, in multivariate settings, the fast Fourier transform offers considerable time savings compared to direct calculation of convolutions.

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