The Accuracy and the Computational Complexity of a Multivariate Binned Kernel Density Estimator

The computational cost of multivariate kernel density estimation can be reduced by prebinning the data. The data are discretized to a grid and a weighted kernel estimator is computed. We report results on the accuracy of such a binned kernel estimator and discuss the computational complexity of the estimator as measured by its average number of nonzero terms.

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Year of publication:	2000
Authors:	Holmström, Lasse
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 72.2000, 2, p. 264-309
Publisher:	Elsevier
Subject:	Kernel density estimation \| binning \| estimation error \| computational complexity

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005221482