Marginal ridge conceptual predictive model selection criterion in linear mixed models

AbstractIn linear mixed model selection under ridge regression, we propose the model selection criteria based on conceptual predictive (Cp) statistic.The first proposed criterion is marginal ridge Cp (MRCp) statistic based on the expected marginal Gauss discrepancy. An improvement of MRCp (IMRCp) statistic is then suggested and demonstrated, which is also an asymptotically unbiased estimator of the expected marginal Gauss discrepancy. Finally, a real data analysis and a Monte Carlo simulation study are given to examine the performance of the proposed criteria.