May W L, Johnson W D
Department of Biometry and Genetics, Louisiana State University Medical Center, New Orleans 70112-1393, USA.
J Biopharm Stat. 1995 Jul;5(2):215-28. doi: 10.1080/10543409508835109.
Methods are proposed for the analysis of data from a multivariate normal distribution when observations are missing completely at random on some of the variates. Park (1) proved the equivalence of the solutions given by maximum likelihood and generalized estimating equations when data are complete and an unstructured covariance is assumed. He suggested that generalized estimating equations may be used if sample sizes are large relative to the amount of missing data and the estimated covariance matrix is positive definite. We give several examples indicating that the estimating equations give results similar to those of maximum likelihood when smoothing of the covariance matrix to eliminate nonpositive definiteness is not encountered as, for example, under an assumption of exchangeable correlation. Generalized linear models are formulated that are appropriate for a wide class of experimental plans.
当某些变量上的观测值完全随机缺失时，本文提出了用于分析来自多元正态分布数据的方法。帕克（1）证明了在数据完整且假设协方差无结构的情况下，最大似然法和广义估计方程给出的解是等价的。他建议，如果相对于缺失数据量而言样本量较大且估计的协方差矩阵是正定的，则可以使用广义估计方程。我们给出了几个例子，表明在未遇到协方差矩阵平滑以消除非正定性的情况下，例如在可交换相关性假设下，估计方程给出的结果与最大似然法的结果相似。本文还构建了适用于广泛实验计划的广义线性模型。
