EFFICIENT ESTIMATION METHODS FOR INFORMATIVE CLUSTER SIZE DATA
EFFICIENT ESTIMATION METHODS FOR INFORMATIVE CLUSTER SIZE DATA
复制标题
信息簇大小数据的有效估计方法
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Kuang‐Yao Lee
中科院分区：
文献类型：
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作者：
Chin‐Tsang Chiang;Kuang‐Yao Lee
基于具有信息簇大小的聚类数据，提出了两种有效的边缘模型估计方法。在我们的程序中，内集群相关性和最小集群大小的信息被充分利用，这是不是与内集群重采样（WCR）和集群加权广义估计方程（CWGEE）方法的情况下。当相关性模型成立且最小聚类数大于1时，所提出的估计量进一步提高了WCR和CWGEE估计量的效率。与WCR估计过程一样，我们的第一种估计方法是计算密集型的。为了克服这个问题，第二个估计方法的发展，其中的估计器是渐近等价的第一个。估计量的渐近性质。第二个估计的有限样本性质进行了研究，通过Monte Carlo模拟的比较与CWGEE估计的数值研究。
Based on clustered data with informative cluster size, two efficient estima- tion methods are proposed for marginal models. In our procedures, the information of within-cluster correlation and minimum cluster size is fully used; this is not the case with the within-cluster re-sampling (WCR) and cluster-weighted generalized estimating equation (CWGEE) methods. When the correlation model is valid and the minimum cluster size is greater than one, the proposed estimatiors further improve the efficiency of the WCR and CWGEE estimators. As withthe WCR estimation procedure, our first estimation method is computationally intensive. To overcome this problem, a second estimation method is developed in which the es- timator is asymptotically equivalent to the first one. Asymptotic properties of the estimators are derived. The finite sample properties of the second estimator are investigated through a Monte Carlo simulation; a comparison with the CWGEE estimator is made in the numerical study.