极端值下的多重插补方法
Multiple Imputation Methods under Extreme Values
AI总结：
本研究评估了极端值对多重插补方法性能的影响，发现基于线性回归的方法在预测性能上表现最佳，强调了在选择插补策略时需考虑极端值、样本量和缺失比例等因素。
摘要：
经验数据库中普遍存在缺失数据，但统计分析通常需要完整数据矩阵。多重插补提供了一种系统的方法来填补这些空缺。本研究评估了几种多重插补方法在存在和不存在极端值情况下的性能，使用R中的MICE包进行评估。通过蒙特卡洛模拟，我们生成了包含三个变量的不完整数据集，并在回归模型中评估了每种插补方法。结果表明，基于线性回归的插补方法在总体预测性能（交叉验证均方误差）上表现最佳，而稀疏模型方法总体效率较低。我们的发现强调了在选择插补策略时极端值的相关性，并突显了样本量、缺失比例、极端值的存在以及拟合模型的类型是性能的关键决定因素。尽管存在局限性，本研究为研究人员提供了实用的建议，强调在选择插补方法之前需要检查缺失机制和极端值的发生情况。
英文摘要：
Missing data are ubiquitous in empirical databases, yet statistical analyses typically require complete data matrices. Multiple imputation offers a principled solution for filling these gaps. This study evaluates the performance of several multiple imputation methods, both in the presence and absence of extreme values, using the MICE package in R. Through Monte Carlo simulations, we generated incomplete data sets with three variables and assessed each imputation method within regression models. The results indicate that the linear regression based imputation method showed the best overall predictive performance (CV-MSE), whereas the sparse model approach was generally less efficient. Our findings underscore the relevance of extreme values when selecting an imputation strategy and highlight sample size, proportion of missingness, presence of extremes, and the type of fitted model as key determinants of performance. Despite its limitations, the study offers practical recommendations for researchers, stressing the need to examine the missingness mechanism and the occurrence of extreme values before choosing an imputation method.
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