Classified generalized linear mixed model prediction incorporating pseudo-prior information

We develop a method of classified mixed model prediction based on generalized linear mixed models that incorporate pseudo-prior information to improve prediction accuracy. We establish consistency of the proposed method both in terms of prediction of the true mixed effect of interest and in terms of correctly identifying the potential class corresponding to the new observations if such a class matching one of the training data classes exists. Empirical results, including simulation studies and real-data validation, fully support the theoretical findings.