AI 中文总结
针对大规模生存数据分析的计算存储挑战，提出用于部分线性可加Cox模型的高效泊松子采样方法，经模拟与淋巴瘤数据集验证，可实现大规模场景下的高效准确统计推断。
AI 中文摘要
为解决大规模生存数据分析中常遇到的计算与存储挑战，我们针对部分线性可加Cox模型提出了一种高效的泊松子采样方法。该模型通过整合线性协变量效应、针对非线性协变量的可加非参数分量以及非参数基准风险函数，提供了一种灵活且可解释的框架。所提方法采用B样条基函数近似非参数分量，并利用去相关得分技术构建基于泊松子采样的估计方程，基于此我们建立了所得估计量的渐近正态性，并根据L-最优准则推导了最优采样概率。此外，我们设计了一种两步自适应算法用于实际实现。该方法无需处理完整数据集即可实现大规模生存分析的高效统计推断，我们通过大量模拟研究和对淋巴瘤癌症数据集的实际应用验证了其性能，证明了其在大规模场景下的效率与准确性。
英文摘要
To address the computational and storage challenges often encountered in large-scale survival data analysis, we propose an efficient Poisson subsampling method for the partially linear additive Cox model. This model provides a flexible yet interpretable framework by incorporating linear covariate effects, additive nonparametric components for nonlinear covariates, and a nonparametric baseline hazard function. The proposed method adopts B-spline basis functions to approximate the nonparametric components and employs the decorrelated score technique to construct a Poisson subsampling-based estimation equation, based on which we establish the asymptotic normality of the resulting estimator and derive the optimal subsampling probabilities according to the L-optimality criterion. Furthermore, we design a two-step adaptive algorithm for practical implementation. The proposed approach enables computationally efficient statistical inference for large-scale survival analysis without processing the full dataset. We validate the performance of the proposed method through extensive simulation studies and a real-world application to a lymphoma cancer dataset, demonstrating its efficiency and accuracy in large-scale settings.
