Marginal ridge conceptual predictive model selection criterion in linear mixed models

Model selection via conditional conceptual predictive statistic for mixed and stochastic restricted ridge estimators in linear mixed models

“…When multicollinearity problems that mentioned in the above paragraph are arose, one or more variables related to the fixed effects usually are deleted, but this could cause some not irrelevant consequences: the fitted candidate model could be misspecified and so, the underfitted or the overfitted candidate models result in large variances for the BLUE as well as a large variance for $$$ {\hat{y}}_i $$$. For this reason, Kuran and Özkale 17,18 enlarged Wenren, 8 Wenren and Shang 10 and Wenren et al's 9 studies for the ridge estimator and the ridge predictor under multicollinearity.…”

“…For this reason, Kuran and Özkale 17,18 enlarged Wenren, 8 Wenren and Shang 10 and Wenren et al's 9 studies for the ridge estimator and the ridge predictor under multicollinearity.…”