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September, 1985 Averaged Shifted Histograms: Effective Nonparametric Density Estimators in Several Dimensions
David W. Scott
Ann. Statist. 13(3): 1024-1040 (September, 1985). DOI: 10.1214/aos/1176349654

Abstract

We introduce two nonparametric multivariate density estimators that are particularly suitable for application in interactive computing environments. These estimators are statistically comparable to kernel methods and computationally comparable to histogram methods. Asymptotic theory of the estimators is presented and examples with univariate and simulated trivariate Gaussian data are illustrated.

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David W. Scott. "Averaged Shifted Histograms: Effective Nonparametric Density Estimators in Several Dimensions." Ann. Statist. 13 (3) 1024 - 1040, September, 1985. https://doi.org/10.1214/aos/1176349654

Information

Published: September, 1985
First available in Project Euclid: 12 April 2007

zbMATH: 0589.62022
MathSciNet: MR803756
Digital Object Identifier: 10.1214/aos/1176349654

Subjects:
Primary: 62G05
Secondary: 62E10

Rights: Copyright © 1985 Institute of Mathematical Statistics

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Vol.13 • No. 3 • September, 1985
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