AI 中文总结
针对异质删失数据，提出结合逆概率加权、M估计和凹成对融合惩罚的方法，开发RISA - ADMM算法，推导估计量理论性质，经模拟和应用验证，可有效识别子组并估计协变量效应。
AI 中文摘要
子组分析在实际中很重要，因为现实世界的数据通常来自异质总体，不同子群体的有意义模式可能有很大差异。正确识别这些子组可提高预测准确性等。大多数现有方法针对完整数据，本文针对异质加速失效时间（AFT）模型下的删失数据提出新的稳健方法。结合逆概率加权、M估计和凹成对融合惩罚来识别子组并估计协变量效应，无需先验子组成员知识。开发了RISA - ADMM算法并证明其收敛性，推导了估计量理论性质。模拟和应用表明该方法稳健有效。
英文摘要
Subgroup analysis is important in practice because real-world data typically come from heterogeneous populations, where meaningful patterns can differ substantially across subpopulations. Correctly identifying these subgroups can improve prediction accuracy, prevent biased or misleading conclusions, and support more effective, targeted decision-making. While most existing subgroup analysis methods are developed for complete data, in this paper we propose a novel and robust approach for censored data under heterogeneous accelerated failure time (AFT) models. Specifically, we combine inverse probability weighting, M-estimation, and concave pairwise fusion penalization to simultaneously identify subgroups and estimate covariate effects for heterogeneous censored data, without requiring prior knowledge of individual subgroup memberships. We further develop an efficient RISA-ADMM algorithm to implement the method and establish its convergence. Furthermore, we derive the theoretical properties of the proposed estimators under mild regularity conditions. Extensive simulations and an application to the German credit dataset demonstrate the robustness and effectiveness of our approach.
Journal refStat, 2026, Volume15, Issue 2, e70158
