Estimation of the mean vector in a singular multivariate normal distribution

Estimation of the mean vector in a singular multivariate normal distribution

This paper addresses the problem of estimating the mean vector of a singular multivariate normal distribution with an unknown singular covariance matrix. The maximum likelihood estimator is shown to be minimax relative to a quadratic loss weighted by the Moore-Penrose inverse of the covariance matrix. An unbiased risk estimator relative to the weighted quadratic loss is provided for a Baranchik...

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

Screening for a Multivariate Mixture Normal Distribution

Minimum Volume Confidence Regions for a Multivariate Normal Mean Vector

Since Stein’s original proposal in 1962, a series of papers have constructed confidence regions of smaller volume than the standard spheres for the mean vector of a multivariate normal distribution. A general approach to this problem is developed here, and used to calculate a lower bound on the attainable volume. Bayes and fiducial methods are involved in the calculation. Scheffe-type problems ...

estimation of the multivariate normal mean under the extended reflected normal loss function