Estimation of mixing proportions in the presence of autoregressively correlated training data:the case of two univariate normal populations

Abstract

This paper considers the estimation of mixing proportions when, in addition to the mixture sample, there are available autoregressively dependent data of known origin from each of the classes which make up the mixture population. An asymptotic variance of the usual discriminant analysis (or confusion matrix) estimator of the mixing proportion is obtained under this condition of correlated training data. The maximum likelihood estimator of the mixing proportion and the associated asymptotic variance are also obtained. A simulation experiment is used to investigate the behaviour of these estimators