Maximum likelihood estimation for multivariate normal with auxiliary information
Author(s): Jianqi Yu, Shaoling Ding and Xiang Wang
Abstract:
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.
Jianqi Yu, Shaoling Ding, Xiang Wang. Maximum likelihood estimation for multivariate normal with auxiliary information. Int J Stat Appl Math 2021;6(4):83-85. DOI: 10.22271/maths.2021.v6.i4b.708
