A FINITE MIXTURE MODEL FOR WORKING CORRELATION MATRICES IN GENERALIZED ESTIMATING EQUATIONS
A FINITE MIXTURE MODEL FOR WORKING CORRELATION MATRICES IN GENERALIZED ESTIMATING EQUATIONS
复制标题
DOI：
10.5705/ss.2010.090
复制
发表时间：
2012-04
影响因子：
1.4
通讯作者：
Lili Xu;N. Lin;Baoxue Zhang;N. Shi
中科院分区：
文献类型：
--
作者：
Lili Xu;N. Lin;Baoxue Zhang;N. Shi
广义估计方程（GEE）方法自Liang和Zeger（1986）提出以来，已被广泛用于纵向数据分析。众所周知，工作相关矩阵的选择会严重影响GEE估计器的效率。为了解决相关的不规范问题，我们提出了一个基于有限混合模型的工作相关性估计器mix-GEE。在温和正则性条件下，如果数据来自高斯混合模型，则mix-GEE估计量是一致的、渐近正态的和渐近有效的。混合- gee方法的一个重要特点是，无论考虑AR(1)还是交换结构，它都保证了估计的工作相关矩阵的正确定性。它在数值上更稳定，并且比混合GEE方法具有更好的有限样本效率（Leung, Wang, and Zhu(2009)）。仿真研究和数据实例进一步证明了本文方法的价值。
The generalized estimating equations (GEE) method has been widely used to analyze longitudinal data since it was proposed by Liang and Zeger (1986). It is well known that the efficiency of the GEE estimator can be seriously affected by the choice of the working correlation matrix. To address the associated misspec- ification issue, we propose an estimator called mix-GEE based on a finite mixture model for the working correlation. Under mild regularity conditions, the mix-GEE estimator is consistent, asymptotically normal, and asymptotically efficient if data are from a Gaussian mixture model. An important feature of the mix-GEE method is that it guarantees the positive definiteness of the estimated working correlation matrix if either the AR(1) or exchangeable structure is included. It is numeri- cally more stable and displays a better finite sample efficiency than the hybrid GEE method (Leung, Wang, and Zhu (2009)). The value of our method is further demonstrated by simulation studies and data examples.