Improving generalised estimating equations using quadratic inference functions
Improving generalised estimating equations using quadratic inference functions
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DOI：
10.1093/biomet/87.4.823
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发表时间：
2000-12-01
期刊：
影响因子：
2.7
通讯作者：
Li, B
中科院分区：
文献类型：
--
作者：
Qu, A;Lindsay, BG;Li, B
广义估计方程使人们能够估计回归参数一致的纵向数据分析，即使当相关结构是错误的。然而，在这样的误设定下，回归参数的估计量可能是低效的。在本文中，我们介绍了一种方法，二次推理功能，不涉及直接估计的相关参数，仍然是最佳的，即使工作相关结构是错误的。其思想是通过基矩阵的线性组合来表示工作相关矩阵的逆，这种表示对于最常用的工作相关性是有效的。渐近理论和模拟表明，在错误指定的工作假设下，这些估计量比广义估计方程的估计量更有效。该方法还提供了用于测试嵌套模型的卡方推断函数和卡方回归误指定测试。此外，无论是否正确指定了工作相关结构，检验统计量都渐近地遵循卡方分布。
Generalised estimating equations enable one to estimate regression parameters consistently in longitudinal data analysis even when the correlation structure is misspecified. However, under such misspecification, the estimator of the regression parameter can be inefficient. In this paper we introduce a method of quadratic inference functions that does not involve direct estimation of the correlation parameter, and that remains,optimal even if the working correlation structure is misspecified. The idea is to represent the inverse of the working correlation matrix by the linear combination of basis matrices, a representation that is valid for the working correlations most commonly used. Both asymptotic theory and simulation show that under misspecified working assumptions these estimators are more efficient than estimators from generalised estimating equations. This approach also provides a chi-squared inference function for testing nested models and a chi-squared regression misspecification test. Furthermore, the test statistic follows a chi-squared distribution asymptotically whether or not the working correlation structure is correctly specified.