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Accession Number:
ADP007142
Title:
Adaptive Probability Density Estimation in Lower Dimensions using Random Tessellations,
Corporate Author:
GEORGE MASON UNIV FAIRFAX VA CENTER FOR COMPUTATIONAL STATISTICS
Report Date:
1992-01-01
Abstract:
This paper presents a class of non-parametric density estimators on a low dimensional space. The support of these estimators is defined by the convex hull of the set of observations. A random sample from the set of observations is used to tessellate the interior of the convex hull. The attribution of empirical probability mass to the tiles resulting from the tessellation produces a density estimate. With a set of appropriate linear constraints on the attribution of mass, the estimator is shown to be a conditional maximum likelihood estimator. Repeating this procedure, and averaging these density estimates within tiles, produces a bootstrap estimate of the density function. The results of this resampling and density estimation process are presented in graphic form.
Supplementary Note:
This article is from 'Computing Science and Statistics: Proceedings of the Symposium on the Interface Critical Applications of Scientific Computing: Biology, Engineering, Medicine, Speech Held in Seattle, Washington on 21-24 April 1991,' AD-A252 938, p241-245.
Pages:
0005
File Size:
0.00MB
