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.
Publié le : 1985-09-14
Classification:
Nonparametric density estimation,
histograms,
frequency polygons,
kernel estimators,
integrated mean squared error,
binned data,
multivariate data analysis,
62G05,
62E10
@article{1176349654,
author = {Scott, David W.},
title = {Averaged Shifted Histograms: Effective Nonparametric Density Estimators in Several Dimensions},
journal = {Ann. Statist.},
volume = {13},
number = {1},
year = {1985},
pages = { 1024-1040},
language = {en},
url = {http://dml.mathdoc.fr/item/1176349654}
}
Scott, David W. Averaged Shifted Histograms: Effective Nonparametric Density Estimators in Several Dimensions. Ann. Statist., Tome 13 (1985) no. 1, pp. 1024-1040. http://gdmltest.u-ga.fr/item/1176349654/