摘要在定期随访的医学研究或临床实验中,人们经常会收集到高维区间删失数据,如何对这类数据进行降维是一个非常有意义的问题.本文基于Kolmogorov-Smirnov检验统计量,利用分割和融合的技巧,把独立特征筛选方法推广到区间删失数据中,提出了一种可以处理超高维II型区间删失数据且不依赖于任何模型假设的变量筛选方法.此方法的适用范围很广,可以有效地处理各种生存模型下的超高维II型区间删失数据,而且可以处理离散型,连续型等多种类型的协变量.在估计生存函数时,本文采用EM-ICM算法,极大地提高了计算效率.大量的数值模拟实验验证了此方法在有限样本下的有效性. | | 服务 | | | 加入引用管理器 | | E-mail Alert | | RSS | 收稿日期: 2020-06-04 | | 基金资助:国家自然科学基金青年项目(No.11901581),中南财经政法大学中央高校基本科研业务费专项资金(No.2722020JCG064)资助. |
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[1] | 刘妍岩, 王蕊, 赵燕, 邹君. 考虑基因与基因间的交互作用的基因组选择方法研究[J]. 应用数学学报, 2019, 42(5): 684-700. | [2] | 连亦旻, 陈钊, 舒明良. SEVIS方法的局部线性估计及其在超高维数据下的应用[J]. 应用数学学报, 2018, 41(1): 1-13. | [3] | 魏中鹏、陈家鼎. {I}型区间删失数据下产品可靠度的置信下限[J]. 应用数学学报(英文版), 2006, 29(1): 81-90. |
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