Optimal prediction in finite populations under matrix loss

Optimal prediction problems in finite population are investigated. Under matrix loss, we provide necessary and sufficient conditions for the linear predictor of a general linearly predictable variable to be the best linear unbiased predictor (BLUP). The essentially unique BLUP of a linearly predictable variable is obtained in the general superpopulation model. Surprisingly, the both BLUPs under matrix and quadratic loss functions are equivalent to each other. Next, we prove that the BLUP is admissible in the class of linear predictors. Conditions for optimality of the simple projection predictor (SPP) are given. Furthermore, the robust SPP and the robust BLUP are characterized on the misspecification of the covariance matrix.