The use of multiple imputation (MI) is becoming increasingly popular for addressing missing data. Although some conventional MI approaches have been well studied and have shown empirical validity, they have limitations when processing large datasets with complex data structures. Their imputation performances usually rely on the proper specification of imputation models, which requires expert knowledge of the inherent relations among variables. Moreover, these standard approaches tend to be computationally inefficient for medium and large datasets. In this paper, we propose a scalable MI framework mixgb, which is based on XGBoost, subsampling, and predictive mean matching. Our approach leverages the power of XGBoost, a fast implementation of gradient boosted trees, to automatically capture interactions and non-linear relations while achieving high computational efficiency. In addition, we incorporate subsampling and predictive mean matching to reduce bias and better account for appropriate imputation variability. The proposed framework is implemented in an R package mixgb. Supplementary materials for this article are available online.

翻译：多重插补方法在应对缺失数据方面越来越受欢迎。虽然一些常规的插补方法已经被研究得比较透彻，并证明在实际中可行，但是在处理具有复杂数据结构的大型数据集时会存在一些限制。这些方法的插补效果通常依赖于对插补模型的适当规范，这需要对变量之间的内在关系有专业知识。此外，这些传统方法在中大型数据集上往往计算效率低下。本文提出了一种可扩展的多重插补框架mixgb，基于 XGBoost、子抽样和预测均值匹配。我们的方法借助 XGBoost 的强大功能，即快速实现梯度增强树，自动捕捉交互和非线性关系，并实现高计算效率。此外，我们还结合子抽样和预测均值匹配方法来降低偏差并更好地考虑适当的插补变异性。所提出的框架在 R 包 mixgb 中实现。本文的补充材料可在线上获得。