Abstract
Classified mixed model prediction (CMMP) is a new method that has embedded the traditional mixed model prediction (MMP) with a modern flavour. The basic idea is to first identify a class among the training data that matches the potential class corresponding to the new observations, whose associated mixed effect is of interest for prediction. Once such a matching is established, the MMP method can be utilized to make more accurate prediction that takes into account the subject-level differences. In this paper, 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. The 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. Remarks and recommendation are offered.
Original language | English (US) |
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Pages (from-to) | 249-261 |
Number of pages | 13 |
Journal | Journal of Statistical Computation and Simulation |
Volume | 92 |
Issue number | 2 |
DOIs | |
State | Published - 2022 |
Keywords
- CMMP
- MSPE
- Sumca
- measure of uncertainty
ASJC Scopus subject areas
- Statistics and Probability
- Modeling and Simulation
- Statistics, Probability and Uncertainty
- Applied Mathematics