Efficient ML Estimation of the Multivariate Normal Distribution from Incomplete Data

Ml Estimation and Lr Tests for the Multivariate Normal

In this study ''Ie investigate maximum likelihood (ML) estimation and likelihood ratio (LR) tests for the multivariate normal distribution with mean vector ~ and covariance matrix k following the linear structures: 11 = Xr< and ]:; = I T G , where X and G are properly defined ~ ~ .-g g~g ~ ~g known matrices, and ~ and ! = (T g) are vectors of unknown parameters. Likelihood equations are obtaine...

The Stein phenomenon for monotone incomplete multivariate normal data

We establish the Stein phenomenon in the context of two-step, monotone incomplete data drawn from Np+q(μ,Σ), a (p+ q)-dimensional multivariate normal population with mean μ and covariance matrix Σ. On the basis of data consisting of n observations on all p+q characteristics and an additional N − n observations on the last q characteristics, where all observations are mutually independent, denot...

Image Reconstruction from Incomplete Data

Estimation of the Multivariate Normal Mean under the Extended Reflected Normal Loss Function

Image Reconstruction from Incomplete Data