Classified Mixed Model Prediction

Jiming Jiang, J. Sunil Rao, Jie Fan, Thuan Nguyen

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Many practical problems are related to prediction, where the main interest is at subject (e.g., personalized medicine) or (small) sub-population (e.g., small community) level. In such cases, it is possible to make substantial gains in prediction accuracy by identifying a class that a new subject belongs to. This way, the new subject is potentially associated with a random effect corresponding to the same class in the training data, so that method of mixed model prediction can be used to make the best prediction. We propose a new method, called classified mixed model prediction (CMMP), to achieve this goal. We develop CMMP for both prediction of mixed effects and prediction of future observations, and consider different scenarios where there may or may not be a “match” of the new subject among the training-data subjects. Theoretical and empirical studies are carried out to study the properties of CMMP, including prediction intervals based on CMMP, and its comparison with existing methods. In particular, we show that, even if the actual match does not exist between the class of the new observations and those of the training data, CMMP still helps in improving prediction accuracy. Two real-data examples are considered. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)269-279
Number of pages11
JournalJournal of the American Statistical Association
Issue number521
StatePublished - Jan 2 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 American Statistical Association.


  • CMMP
  • Future observation
  • Linear mixed model
  • Mean squared prediction error
  • Mixed effects
  • Prediction interval


Dive into the research topics of 'Classified Mixed Model Prediction'. Together they form a unique fingerprint.

Cite this