针对小样本生存分析难题，提出新型贝叶斯集成树模型提升预测稳定性。
Generalized Bayesian Ensemble Survival Tree (GBEST) model
- 融合贝叶斯自助法与贝塔·斯蒂西法构建集成树模型
- 在模拟与真实数据上均优于传统生存模型的预测性能
- 开源实现便于研究者复现与应用
本文提出一种新型生存分析预测模型——广义贝叶斯集成生存树（GBEST）。该模型针对小样本及删失机制下的生存分析挑战，创新性地将贝叶斯自助法与贝塔·斯蒂西贝叶斯自助法结合，应用于袋装树模型以处理删失数据。实验证明，在模拟数据和真实数据上，该方法在预测性能与结果稳定性方面均优于现有经典生存模型。方法学上，首次将近期贝叶斯集成思想拓展至生存数据分析领域，构建了名为GBEST的新模型。计算层面，已实现GBEST的R语言版本，并公开于GitHub仓库，支持社区使用与扩展。
原文摘要 · Abstract (English)
This paper proposes a new class of predictive models for survival analysis called Generalized Bayesian Ensemble Survival Tree (GBEST). It is well known that survival analysis poses many different challenges, in particular when applied to small data or censorship mechanism. Our contribution is the proposal of an ensemble approach that uses Bayesian bootstrap and beta Stacy bootstrap methods to improve the outcome in survival application with a special focus on small datasets. More precisely, a novel approach to integrate Beta Stacy Bayesian bootstrap in bagging tree models for censored data is proposed in this paper. Empirical evidence achieved on simulated and real data underlines that our approach performs better in terms of predictive performances and stability of the results compared with classical survival models available in the literature. In terms of methodology our novel contribution considers the adaptation of recent Bayesian ensemble approaches to survival data, providing a new model called Generalized Bayesian Ensemble Survival Tree (GBEST). A further result in terms of computational novelty is the implementation in R of GBEST, available in a public GitHub repository.
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