Second-order generalized estimating equations for correlated count data
Second-order generalized estimating equations for correlated count data
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DOI：
10.1007/s00180-015-0599-1
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发表时间：
2016-06-01
影响因子：
1.3
通讯作者：
Kassahun, Wondwosen
中科院分区：
文献类型：
--
作者：
Kalema, George;Molenberghs, Geert;Kassahun, Wondwosen
广义估计方程已广泛用于相关计数数据的分析。求解这些方程会产生一致的参数估计，而估计的方差是从夹心估计器获得的，从而确保即使在所谓的工作相关矩阵错误指定的情况下，也可以对边际均值参数得出有效的推论。然而，它们允许错误指定工作相关结构，这意味着如果科学兴趣也存在于协方差或相关结构中，则这些方程存在局限性。我们在此提出这些估计方程的扩展，使得通过合并二元泊松分布，可以对响应向量的方差-协方差矩阵进行适当建模，这将允许对其进行推断。三明治估计器用于标准误差，确保对估计参数进行合理的推断。提出了两个应用。
Generalized estimating equations have been widely used in the analysis of correlated count data. Solving these equations yields consistent parameter estimates while the variance of the estimates is obtained from a sandwich estimator, thereby ensuring that, even with misspecification of the so-called working correlation matrix, one can draw valid inferences on the marginal mean parameters. That they allow misspecification of the working correlation structure, though, implies a limitation of these equations should scientific interest also be in the covariance or correlation structure. We propose herein an extension of these estimating equations such that, by incorporating the bivariate Poisson distribution, the variance-covariance matrix of the response vector can be properly modelled, which would permit inference thereon. A sandwich estimator is used for the standard errors, ensuring sound inference on the parameters estimated. Two applications are presented.