Assessing Uncertainty for Classified Mixed Model Prediction

Oregon Convention Center 

Classified mixed model prediction (CMMP) is a new method that has embedded the traditional mixed model prediction (MMP) with a modern flavor. In this work, we consider estimation of the mean squared prediction error (MSPE) of CMMP. A recently proposed Sumca method is implemented. Sumca combines analytic and Monte-Carlo approaches, leading to a second-order unbiased estimator of the MSPE. Performance of Sumca is investigated via simulation studies, and comparisons are made with alternative methods. The simulation study shows that a brute-force bootstrap method performs almost as well as Sumca, while a naive approach and a Prasad-Rao estimator at the matched index are significantly inferior to Sumca. A real-data application is considered. This work is joint with Jiming Jiang of the University of California, Davis, USA and J. Sunil Rao of the University of Miami, USA.