Constraints on Random Effects and Mixed Model Predictions
In linear mixed models theory one is assumed to know the structure of random effects covariance matrix. The suggestions are sometimes contradictious, especially if the model includes interactions between fixed effects and random effects. Mols (2003) presented conditions under which two different random effects variance matrices will yield equal estimation and prediction results during the paper it is assumed that X is of full column rank. Wang (2010)~(11) weakened the conditions of his theorem, and obtained the same results as his. Wang (2010)~(12) extended Molss (2003) results to situation that X is deficient in rank. We give a series of results in this paper. They are all necessary and sufficient theorems.
linear mixed models best linear unbiased estimator best linear unbiased predictor
WANG Shiqing MA Ying
College of Mathematics and Information Sciences, North China University of Water Conservancy and Electric Power, Zhengzhou, P.R.China, 450011
国际会议
威海
英文
499-502
2010-07-24（万方平台首次上网日期，不代表论文的发表时间）