Maximum likelihood theory for a class of independently, but nonidentically distributed observations

Abstract

In this paper, maximum likelihood estimates of the mean and the covariance of a normal random variable, based on a set of independently, but nonidentically distributed observations, are discussed. An efficient algorithm for computing MLEs is introduced. The asymptotic properties such as strong consistency and asymptotic normality are examined.