An application of linear feature selection to estimation of proportions

Abstract Let X be a random n-vector whose density function is given by a mixture of known multivariate normal density functions where the corresponding mixture proportions (a priori probabilities) are unknown. We present a numerically tractable method for obtaining estimates of the mixture proportions based on the linear feature selection technique of Guseman, Peters and Walker (1975). Keywords: multivariate normal populationsBayesian classificationprobability of misclassificationunbiased estimatesconstrained least squares