Assessing uncertainty for classified mixed model prediction

Thuan Nguyen, Jiming Jiang, J. Sunil Rao

Research output: Contribution to journalArticlepeer-review


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 languageEnglish (US)
Pages (from-to)249-261
Number of pages13
JournalJournal of Statistical Computation and Simulation
Issue number2
StatePublished - 2022


  • CMMP
  • MSPE
  • Sumca
  • measure of uncertainty

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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