Annals of Statistics

Averaged Shifted Histograms: Effective Nonparametric Density Estimators in Several Dimensions

David W. Scott

Full-text: Open access

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.

Article information

Source
Ann. Statist., Volume 13, Number 3 (1985), 1024-1040.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176349654

Digital Object Identifier
doi:10.1214/aos/1176349654

Mathematical Reviews number (MathSciNet)
MR803756

Zentralblatt MATH identifier
0589.62022

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 62E10: Characterization and structure theory

Keywords
Nonparametric density estimation histograms frequency polygons kernel estimators integrated mean squared error binned data multivariate data analysis

Citation

Scott, David W. Averaged Shifted Histograms: Effective Nonparametric Density Estimators in Several Dimensions. Ann. Statist. 13 (1985), no. 3, 1024--1040. doi:10.1214/aos/1176349654. https://projecteuclid.org/euclid.aos/1176349654


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