Abstract
We develop a new method for classified mixed model prediction (CMMP). The original CMMP method (Jiang et al., 2018b) does not incorporate covariate information in matching the class between the new observations and the training data. As a result, the method may not outperform the mixed model prediction (MMP) method in terms of predictive performance. The new CMMP method that we develop utilizes covariate information, and therefore is more accurate in terms of the matching. We show that the new CMMP method outperforms the MMP in terms of the predictive performance. Furthermore, we develop a second-order unbiased estimator of the mean squared prediction error (MSPE) for the new CMMP, which was previously not available for the original CMMP. Theoretical and empirical properties of the proposed new CMMP method as well as the MSPE estimator are studied. A real data application is considered. (C) 2019 Elsevier B.V. All rights reserved.