ML estimation of mean and covariance structures with missing data using complete data routines
ML estimation of mean and covariance structures with missing data using complete data routines
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DOI：
10.2307/1165260
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发表时间：
1999-03-01
影响因子：
2.4
通讯作者：
Bentler, PM
中科院分区：
文献类型：
--
作者：
Jamshidian, M;Bentler, PM
当数据缺失时，我们考虑均值和协方差结构模型的最大似然估计。描述了用于参数估计的期望最大化(EM)、广义期望最大化(GEM)、Fletcher-Powell和Fisher-Sewing算法。它展示了如何利用软件中处理完整数据问题的机制来实现每种算法。给出了一种求取观测信息矩阵和标准误差的数值微分法。该方法还使用了完整的数据编程机制。讨论了检验假设的似然比检验方法。用三个算例比较了上述四种算法的成本，并说明了所考虑的标准误差估计和假设检验。使用三个完全随机缺失(MCAR)、随机缺失(MAR)和非MCAR或MAR的人工数据集，研究了ML估计以及均值推定和列表删除估计对缺失数据机制的敏感性。
We consider maximum likelihood (ML) estimation of mean and covariance structure models when data are missing. Expectation maximization (EM), generalized expectation maximization (GEM), Fletcher-Powell, and Fisher-sewing algorithms are described for parameter estimation. It is shown how the machinery within a software that handles the complete data problem can be utilized to implement each algorithm. A numerical differentiation method for obtaining the observed information matrix and the standard errors is given. This method also uses the complete data program machinery. The likelihood ratio test is discussed for testing hypotheses. Three examples are used to compare the cost of the four algorithms mentioned above, as well as to illustrate the standard error estimation and the test of hypothesis considered. The sensitivity of the ML estimates as well as the mean imputed and listwise deletion estimates to missing data mechanisms is investigated using three artificial data sets that are missing completely at random (MCAR), missing at random (MAR), and neither MCAR nor MAR.