具有随机约束的混合效应模型参数的岭型谱分解估计（英文）

Ridge-type spectral decomposition estimators in mixed effects models with stochastic restrictions

对于具有随机线性约束的线性混合效应模型参数提出一种称之为条件岭型谱分解估计的方法.利用均方误差矩阵和广义均方误差对固定效应参数的几种估计量进行比较,给出条件岭型谱分解估计优于条件谱分解估计的充分条件,并给出这两种估计的相对效率的上下界.最后,模拟算例验证了理论结果的正确性.

This paper proposes a new estimation of fixed effects in linear mixed models with stochastic restrictions, which is called a conditional ridge-type spectral decomposition estimator. Using the mean squared error matrix and generalized mean squared error as criteria for comparing the estimates, we establish sufficient conditions for the superiority of the conditional ridge-type spectral de-composition estimator over the conditional spectral decomposition estimator. The upper and lower bounds of the relative efficiency are also given. Finally,a simulation example is given to illustrate the theoretical results.