Rank-Based Greedy Model Averaging for High-Dimensional Survival Data
成果类型：
Article
署名作者：
He, Baihua; Ma, Shuangge; Zhang, Xinyu; Zhu, Li-Xing
署名单位：
Chinese Academy of Sciences; University of Science & Technology of China, CAS; Yale University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Beijing Normal University; Beijing Normal University Zhuhai; Hong Kong Baptist University
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2022.2070070
发表日期：
2023
页码：
2658-2670
关键词：
confidence-intervals
selection
Consistency
摘要：
Model averaging is an effective way to enhance prediction accuracy. However, most previous works focus on low-dimensional settings with completely observed responses. To attain an accurate prediction for the risk effect of survival data with high-dimensional predictors, we propose a novel method: rank-based greedy (RG) model averaging. Specifically, adopting the transformation model with splitting predictors as working models, we doubly use the smooth concordance index function to derive the candidate predictions and optimal model weights. The final prediction is achieved by weighted averaging all the candidates. Our approach is flexible, computationally efficient, and robust against model misspecification, as it neither requires the correctness of a joint model nor involves the estimation of the transformation function. We further adopt the greedy algorithm for high dimensions. Theoretically, we derive an asymptotic error bound for the optimal weights under some mild conditions. In addition, the summation of weights assigned to the correct candidate submodels is proven to approach one in probability when there are correct models included among the candidate submodels. Extensive numerical studies are carried out using both simulated and real datasets to show the proposed approach's robust performance compared to the existing regularization approaches. Supplementary materials for this article are available online.