APPLICATIONS OF MULTIPLE IMPUTATION TO THE ANALYSIS OF CENSORED REGRESSION DATA
APPLICATIONS OF MULTIPLE IMPUTATION TO THE ANALYSIS OF CENSORED REGRESSION DATA
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DOI：
10.2307/2532387
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发表时间：
1991-12-01
期刊：
影响因子：
1.9
通讯作者：
TANNER, MA
中科院分区：
文献类型：
--
作者：
WEI, GCG;TANNER, MA
文章的第一部分回顾了数据增强算法，并提出了两个近似的数据增强算法的缺失数据问题的分析：穷人的数据增强算法和渐近数据增强算法。 这两个算法，然后在删失回归数据的上下文中实现，以获得半参数方法。 删失回归算法的性能进行了研究，在模拟研究。 据发现，到研究的精度，穷人的和渐近数据增强估计，以及巴克利-詹姆斯估计的偏差，似乎没有从零不同。 然而，关于均方误差，在本模拟研究中检查的各种设置中，两个数据增强估计量的均方误差小于Buckley-James估计量。 此外，与两个数据增强估计量相关联的是用于估计估计回归参数的标准误差的自然设备。 它显示了如何使用该设备来估计任何参数的数据增强估计的标准误差（例如，相关系数）。 在模拟研究中，发现回归参数的渐近数据增强估计的估计标准误与相应参数估计的Monte Carlo标准差一致。 使用更新的斯坦福大学心脏移植数据集的算法进行说明。
The first part of the article reviews the Data Augmentation algorithm and presents two approximations to the Data Augmentation algorithm for the analysis of missing-data problems: the Poor Man's Data Augmentation algorithm and the Asymptotic Data Augmentation algorithm. These two algorithms are then implemented in the context of censored regression data to obtain semiparametric methodology. The performances of the censored regression algorithms are examined in a simulation study. It is found, up to the precision of the study, that the bias of both the Poor Man's and Asymptotic Data Augmentation estimators, as well as the Buckley-James estimator, does not appear to differ from zero. However, with regard to mean squared error, over a wide range of settings examined in this simulation study, the two Data Augmentation estimators have a smaller mean squared error than does the Buckley-James estimator. In addition, associated with the two Data Augmentation estimators is a natural device for estimating the standard error of the estimated regression parameters. It is shown how this device can be used to estimate the standard error of either Data Augmentation estimate of any parameter (e.g., the correlation coefficient) associated with the model. In the simulation study, the estimated standard error of the Asymptotic Data Augmentation estimate of the regression parameter is found to be congruent with the Monte Carlo standard deviation of the corresponding parameter estimate. The algorithms are illustrated using the updated Stanford heart transplant data set.