Second-order unbiased naive estimator of mean squared error for EBLUP in small-area estimation

Abstract: An empirical best linear unbiased prediction (EBLUP) estimator is utilized for efficient inference in small-area estimation. To measure its uncertainty, we need to estimate its mean squared error (MSE) since the true MSE cannot generally be derived in a closed form. The "naive MSE estimator", one of the estimators available for small-area inference, is unlikely to be chosen, since it does not achieve the desired asymptotic property, namely second-order unbiasedness, although it maintains strict positivity and tractability. Therefore, users tend to choose the second-order unbiased MSE estimator. In this paper, we seek a new adjusted maximum-likelihood method to obtain a naive MSE estimator that achieves the required asymptotic property. To obtain the result, we also reveal the relationship between the general adjusted maximum-likelihood method for the model variance parameter and the general functional form of the second-order unbiased, and strictly positive, MSE estimator. We also compare the performance of the new method with that of the existing naive estimator through a Monte Carlo simulation study. The results show that the new method remedies the underestimation associated with the existing naive estimator.