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
Personal Author(s) : Hearne, Leonard B. ; Wegman, Edward J.
Report Date : 1992
Pagination or Media Count : 5
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.
Descriptors : *FUNCTIONS, *GRAPHICS, *PROBABILITY, DENSITY, ESTIMATES, MASS, PAPER, TILES.
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE