用机器学习提升右删失数据下的两样本检验效果
Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study
- 构建集成模型融合经典检验方法预测结果
- 18种方法对比显示新模型统计功效更高
- 适合生物统计、生存分析领域研究者使用
本研究评估机器学习方法在右删失数据下的两样本检验有效性。提出多种具有不同架构的基于机器学习的方法，并作为两样本检验工具实现。所有方法均为集成（堆叠）模型，结合经典两样本检验的预测结果。本文展示所提方法的训练结果，比较其与经典方法的统计功效，分析原假设成立时的零分布特性，并评估所引入特征的重要性。共涵盖18种右删失数据下的两样本检验方法，包括所提方法与经典方法。所有数值实验结果均来自通过逆变换采样生成的合成数据集，并经多次蒙特卡洛模拟验证。相关代码、数据集及模型已开源于GitHub和Hugging Face。
原文摘要 · Abstract (English)
The focus of this study is to evaluate the effectiveness of Machine Learning (ML) methods for two-sample testing with right-censored observations. To achieve this, we develop several ML-based methods with varying architectures and implement them as two-sample tests. Each method is an ensemble (stacking) that combines predictions from classical two-sample tests. This paper presents the results of training the proposed ML methods, examines their statistical power compared to classical two-sample tests, analyzes the null distribution of the proposed methods when the null hypothesis is true, and evaluates the significance of the features incorporated into the proposed methods. In total, this work covers 18 methods for two-sample testing under right-censored observations, including the proposed methods and classical well-studied two-sample tests. All results from numerical experiments were obtained from a synthetic dataset generated using the inverse transform sampling method and replicated multiple times through Monte Carlo simulation. To test the two-sample problem with right-censored observations, one can use the proposed two-sample methods (scripts, dataset, and models are available on GitHub and Hugging Face).
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