论文标题
通过后验预测检查评估多个插补模型的图形和数值诊断工具
Graphical and numerical diagnostic tools to assess multiple imputation models by posterior predictive checking
论文作者
论文摘要
丢失的数据通常处理多个插补。多个插补过程的关键部分是选择明智的模型来生成不完整数据的合理值。提出了一种基于后验预测检查的方法来诊断基于后验预测检查的插补模型。为了评估插补模型的友好性，提出的诊断方法将观察到的数据与相应后验预测分布产生的复制物进行比较。如果插补模型与实体模型相似，则预期观察到的数据将位于相应的预测后验分布的中心。仿真和应用旨在研究参数和半参数插补方法，连续和离散的不完整变量，单变量和多变量缺失模式的拟议诊断方法。结果显示了提出的诊断方法的有效性。
Missing data are often dealt with multiple imputation. A crucial part of the multiple imputation process is selecting sensible models to generate plausible values for incomplete data. A method based on posterior predictive checking is proposed to diagnose imputation models based on posterior predictive checking. To assess the congeniality of imputation models, the proposed diagnostic method compares the observed data with their replicates generated under corresponding posterior predictive distributions. If the imputation model is congenial with the substantive model, the observed data are expected to be located in the centre of corresponding predictive posterior distributions. Simulation and application are designed to investigate the proposed diagnostic method for parametric and semi-parametric imputation approaches, continuous and discrete incomplete variables, univariate and multivariate missingness patterns. The results show the validity of the proposed diagnostic method.
