You are not currently logged in.
Access your personal account or get JSTOR access through your library or other institution:
Fast Computation of Multivariate Kernel Estimators
M. P. Wand
Journal of Computational and Graphical Statistics
Vol. 3, No. 4 (Dec., 1994), pp. 433-445
Published by: Taylor & Francis, Ltd. on behalf of the American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of America
Stable URL: http://www.jstor.org/stable/1390904
Page Count: 13
Preview not available
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.
Journal of Computational and Graphical Statistics © 1994 American Statistical Association