This paper considers the problem of estimating a covariance matrix under Stein’s loss. Sufficient conditions for the modified Efron–Morris estimator to be minimax under weighted quadratic loss are shown, which provide a general method for improving the estimator of the covariance matrix.

We investigate the problem of estimating the Cholesky decomposition in a conditional independent normal model with missing data. Explicit expressions for the maximum likelihood estimators and unbiased estimators are derived. By introducing a special group, we obtain the best equivariant estimators.

This paper quantitatively evaluates the potential impacts of removing China's Hukou system on the world economy. By denying migrant workers the right to health benefits and housing, China's Household Registration (Hukou) system presents a significant distortion to the Chinese labor market that...