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Model-free conditional screening for ultrahigh-dimensional survival data via conditional distance correlation

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成果类型:
期刊论文
作者:
Cui, Hengjian;Liu, Yanyan;Mao, Guangcai;Zhang, Jing
通讯作者:
Jing Zhang<&wdkj&>Jing Zhang Jing Zhang Jing Zhang
作者机构:
[Cui, Hengjian] Capital Normal Univ, Sch Math Sci, Beijing, Peoples R China.
[Liu, Yanyan] Wuhan Univ, Sch Math & Stat, Wuhan, Hubei, Peoples R China.
[Mao, Guangcai] Cent China Normal Univ, Sch Math & Stat, Wuhan, Hubei, Peoples R China.
[Zhang, Jing] Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan, Hubei, Peoples R China.
[Zhang, Jing] Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan 430073, Hubei, Peoples R China.
通讯机构:
[Jing Zhang; Jing Zhang Jing Zhang Jing Zhang] S
School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei, China
语种:
英文
关键词:
conditional distance correlation;model-free screening;sure screening property;ultrahigh-dimensional survival data
期刊:
BIOMETRICAL JOURNAL
ISSN:
0323-3847
年:
2023
卷:
65
期:
3
页码:
2200089-
基金类别:
The authors would like to thank the Editor, the Associate Editor, and the two reviewers for their constructive and insightful comments and suggestions that greatly improved the paper. This work is supported by the State Key Program of the National Natural Science Foundation of China (No:12031016), National Natural Science Foundation of China (No:11971324,11971362,11901581,12101256), Natural Science Foundation of Hubei Province (No:2021CFB502), the Interdisciplinary Construction of Bioinformatics and Statistics, and the Academy for Multidisciplinary Studies, Capital Normal University.
机构署名:
本校为其他机构
院系归属:
数学与统计学学院
摘要:
How to select the active variables that have significant impact on the event of interest is a very important and meaningful problem in the statistical analysis of ultrahigh-dimensional data. In many applications, researchers often know that a certain set of covariates are active variables from some previous investigations and experiences. With the knowledge of the important prior knowledge of active variables, we propose a model-free conditional screening procedure for ultrahigh dimensional survival data based on conditional distance correlation. The proposed procedure can effectively detect t...

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