Maximum likelihood estimation for multivariate normal with auxiliary information
Author(s): Jianqi Yu, Shaoling Ding and Xiang Wang
Closed forms are obtained for the maximum likelihood estimators (MLE) of the mean vectors and the covariance matrix of a multivariate normal samples using known means of auxiliary variables. The likelihood function is decomposed as product of several independent normal and conditional normal likelihood functions. The parameters are transformed into a new set of parameters of which the MLEs are easy to derive. Since the MLE are invariant, the MLE of the original parameters are derived using the inverse transformation.