Borrowing information from populations with similar structural mortality patterns and trajectories has been well recognized as an useful strategy to the mortality forecasting of a target population. This article presents a flexible framework for the selection of populations from a given candidate pool to assist a target population in mortality forecasting. The defining feature of the framework is the deletion-substitution-addition (DSA) algorithm, which is entirely data driven and versatile to work with any multiple-population model for mortality prediction. In numerical studies, the framework with an extended augmented common factor model is applied to the Human Mortality Database, and the superiority of the proposed framework is evident in mortality forecasting performance.

从具有相似结构死亡率模式和轨迹的人群中借用信息已被公认为对目标人群进行死亡率预测的有用策略。本文提出了一个灵活的框架，用于从给定的候选池中选择人群，以帮助目标人群进行死亡率预测。该框架的定义特征是删除-替代-添加 (DSA) 算法，该算法完全是数据驱动的，并且可以与任何多人口模型一起用于死亡率预测。在数值研究中，将具有扩展的增广公因子模型的框架应用于人类死亡率数据库，该框架在死亡率预测性能方面的优越性显而易见。