Using Principal Components as Auxiliary Variables in Missing Data Estimation
Using Principal Components as Auxiliary Variables in Missing Data Estimation
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DOI：
10.1080/00273171.2014.999267
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发表时间：
2015-05-04
影响因子：
3.8
通讯作者：
Little, Todd D.
中科院分区：
文献类型：
--
作者：
Howard, Waylon J.;Rhemtulla, Mijke;Little, Todd D.
为了处理由于参与者无应答或流失而产生的缺失数据，方法学家建议采用一种包容性策略，其中使用大量辅助变量来告知缺失数据过程。在实践中，可能的辅助变量的集合通常太大。我们建议使用主成分分析（PCA），以减少可能的辅助变量的数量，以便于管理的数量。一系列的蒙特卡罗模拟比较了包含策略与八个辅助变量（包含方法）的性能，PCA策略只使用一个来自八个原始变量的主成分（PCA方法）。我们研究了四个自变量的影响：相关性的大小，缺失数据率，缺失数据机制，和样本量对参数偏差，均方根误差，置信区间覆盖。结果表明，PCA方法的结果在无偏的参数估计和潜在的更高的精度比包容性的方法。我们的结论是，使用PCA策略，以减少辅助变量的数量是一种有效的和实用的方法来获得的好处，在许多可能的辅助变量的存在下的包容性策略。
To deal with missing data that arise due to participant nonresponse or attrition, methodologists have recommended an inclusive strategy where a large set of auxiliary variables are used to inform the missing data process. In practice, the set of possible auxiliary variables is often too large. We propose using principal components analysis (PCA) to reduce the number of possible auxiliary variables to a manageable number. A series of Monte Carlo simulations compared the performance of the inclusive strategy with eight auxiliary variables (inclusive approach) to the PCA strategy using just one principal component derived from the eight original variables (PCA approach). We examined the influence of four independent variables: magnitude of correlations, rate of missing data, missing data mechanism, and sample size on parameter bias, root mean squared error, and confidence interval coverage. Results indicate that the PCA approach results in unbiased parameter estimates and potentially more accuracy than the inclusive approach. We conclude that using the PCA strategy to reduce the number of auxiliary variables is an effective and practical way to reap the benefits of the inclusive strategy in the presence of many possible auxiliary variables.