纵向数据下广义经验似然方法的有效稳健估计

基于广义经验似然估计方法,提出了一种有效且稳健的估计,实现对纵向数据在线性模型下均值和协方差矩阵的联合估计。利用Cholysky分解将模型重参数化,利用拉格朗日乘子法求出估计值,再还原出均值和协方差矩阵的估计。在模拟研究中将所提方法同文献中其他稳健估计进行比较,结果显示所提方法效率更高。最后将所提方法用于分析CD4细胞数据,交叉验证结果显示所提方法更加可靠。

Effective Robust Estimation Based on the Generalized Empirical Likelihood Method for Longitudinal Data

Based on the generalized empirical likelihood estimation method, a robust and effective estimation was proposed to realize the joint estimation of the mean and covariance matrix of longitudinal data in linear models. Using the Cholysky decomposition, the model was re-parameterized, and using the Lagrangian multiplier method, estimates were obtained, then, an estimate of the mean and covariance matrix was restored. Comparing the proposed method with other robust estimates in the literature in a simulation study, the results show that the proposed method is more efficient. Finally, the proposed method was used to analyze CD4 cell data. Cross-validation results show that the proposed method is more reliable.