Volume no :26, Issue no: 1, December (2021)

Journal of Statistics: Advances in Theory and Applications

MAXIMUM LIKELIHOOD ESTIMATION FOR MULTIVARIATE NORMAL POPULATION WITH MISSING DATA USING AUXILIARY INFORMATION

Author's: Jianqi Yu, Shaoling Ding and Xiang Wang
Pages: [1] - [11]
Received Date: July 29, 2021
Submitted by:
DOI: http://dx.doi.org/10.18642/jsata_7100122218

Abstract

Closed forms are obtained for maximum likelihood estimates (MLE) of multivariate normal with missing data using auxiliary information. The likelihood function is obtained 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 MLEs are invariant, the MLEs of the original parameters are derived using the inverse transformation.

Keywords

auxiliary variables, incomplete data, maximum likelihood estimator.