Uniqueness of estimation and identifiability in mixture models
Bruce G. Lindsay, Kathryn Roeder · Canadian Journal of Statistics · 1993
Further properties of the nonparametric maximum-likelihood estimator of a mixing distribution are obtained by exploiting the properties of totally positive kernels. Sufficient conditions for uniqueness of the estimator are given. This result is more general, and the proof is substantially simpler, than given previously. When the component density has support on N points, it is shown that all identifiable mixing distributions have support on no more than N/2 points. Identifiable mixtures are shown to lie on the boundary of the mixture model space. The maximum-likelihood estimate is shown to be unique if the vector of observations lies outside this space.