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高维生存数据的删失复合条件分位数筛选

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本文提出了一种删失复合条件分位数系数(cCCQC),用于评估高维删失回归模型中各预测变量的相对重要性.cCCQC 利用了跨分位数的所有有用信息,能够有效地检测非线性效应,包括交互作用和异质性.此外,基于 cCCQC 的筛选方法对异常值具有鲁棒性,并具有确定筛选性质.模拟结果表明,该方法在高维预测变量的生存数据集中表现良好,尤其是在变量高度相关的情况下.
Censored Composite Conditional Quantile Screening for High-Dimensional Survival Data
In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening prop-erty.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.

high-dimensional survival datacensored composite conditional quantile coefficientsure screening propertyrank consistency property

刘薇、李应求

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湖南财政经济学院数学与统计学院,长沙,410205

长沙理工大学数学与统计学院,长沙,410114

高维生存数据 删失复合条件分位数系数 特征筛选 确定筛选性质 排序相合性

Outstanding Youth Foundation of Hunan Provincial Department of Education

22B0911

2024

10.3969/j.issn.10.12460.aps.2024.2022074

应用概率统计
中国数学会概率统计学会

应用概率统计

CSTPCD北大核心
影响因子：0.263
ISSN：1001-4268
年,卷(期)：2024.40(5)