MIXTURE DECOMPOSITION FOR DISTRIBUTIONS FROM THE EXPONENTIAL FAMILY USING A GENERALIZED-METHOD OF MOMENTS

A finite mixture distribution consists of the superposition of a finit e number of component probability densities, and is typically used to model a population composed of two or more subpopulations. Mixture mod els find utility in situations where there is a difficulty in directly observing the underlying components of the population of interest, Th is paper examines the method of moments as a general estimation techni que for estimating the parameters of the component distributions and t heir mixing proportions, It is shown that the same basic solution can be applied to any continuous or discrete density from the exponential family with a known common shape parameter, Results of an empirical st udy of the method are also presented.