LONGITUDINAL DATA-ANALYSIS USING GENERALIZED LINEAR-MODELS
LONGITUDINAL DATA-ANALYSIS USING GENERALIZED LINEAR-MODELS
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DOI：
10.2307/2336267
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发表时间：
1986-04-01
期刊：
影响因子：
2.7
通讯作者：
ZEGER, SL
中科院分区：
文献类型：
--
作者：
LIANG, KY;ZEGER, SL
本文提出了广义线性模型在纵向数据分析中的推广。我们引入了一类估计方程，在对时间依赖性的温和假设下给出回归参数及其方差的一致估计。估计方程的推导没有指定受试者观察的联合分布，但它们可以简化为多元高斯结果的得分方程。给出了一般估计量的渐近理论。具体的情况下，我们假设独立，m-依赖和交换的相关结构，从每一个主题进行了讨论。考虑了两种简单情况下所提估计器的效率。该方法与准似然密切相关。
This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject''s observations yet they reduce to the score equations for multivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m-dependence and exchangeable correlation structures from each subject are discussed. Efficiency of the proposed estimators in two simple situations is considered. The approach is closely related to quasi-likelihood.