Implementation and Applications of Bivariate Gaussian Mixture Decomposition

Abstract

An interactive method for decomposing mixtures consisting of an arbitrary number of bivariate Gaussian components is described, which can handle problems currently attacked by cluster analysis methods. In contradistinction to most clustering methods, this procedure does not require selection of a metric or distance function with sample element arguments. Instead, estimates of population bivariate contours are examined graphically to yield estimates of subpopulation parameters. This approach is based on properties of the underlying population rather than on heuristic measures of distance between elements of a sample. Besides discussing the theory underlying this new class of procedures, several examples involving real and simulated data are presented.