Accession Number : ADP007168
Title : Variance-Reducing Kernels for Mixture Decomposition,
Corporate Author : CALIFORNIA UNIV BERKELEY DEPT OF BIOMEDICAL AND ENVIRONMENTAL HEALTH SCIENCE
Personal Author(s) : Lock, Michael D. ; Tarter, Michael E. ; Mellin, Christina C.
Report Date : 1992
Pagination or Media Count : 4
Abstract : Methodology is described for constructing kernels for the purpose of identifying and separating the components of a mixture of densities. One such kernel has the property of reducing the variance of the individual subcomponents of a mixture thereby making them more visible. A second method based on a weighted version of the Mean Integrated Square Error metric takes advantage of the properties of mixtures comprised of densities with differing location parameters. The resulting kernel focuses alternatively on either the right or the left side of the variate support region. Combined with the variance-reducing kernel, this procedures enhances the estimation of either the leftmost or right most mixture subcomponent.
Descriptors : *STATISTICS, ERRORS, MEAN, METHODOLOGY, MIXTURES, PARAMETERS, REGIONS.
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE