Feature Screening for Ultrahigh-dimensional Censored Data with Varying Coefficient Single-index Model  

作　　者：Yi LIU 

机构地区：[1]School of Mathematical Sciences, Ocean University of China

出　　处：《Acta Mathematicae Applicatae Sinica》2019年第4期845-861,共17页应用数学学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.11801567)

摘　　要：In this paper, we study the sure independence screening of ultrahigh-dimensional censored data with varying coefficient single-index model. This general model framework covers a large number of commonly used survival models. The property that the proposed method is not derived for a specific model is appealing in ultrahigh dimensional regressions, as it is difficult to specify a correct model for ultrahigh dimensional predictors.Once the assuming data generating process does not meet the actual one, the screening method based on the model will be problematic. We establish the sure screening property and consistency in ranking property of the proposed method. Simulations are conducted to study the finite sample performances, and the results demonstrate that the proposed method is competitive compared with the existing methods. We also illustrate the results via the analysis of data from The National Alzheimers Coordinating Center(NACC).In this paper, we study the sure independence screening of ultrahigh-dimensional censored data with varying coefficient single-index model. This general model framework covers a large number of commonly used survival models. The property that the proposed method is not derived for a specific model is appealing in ultrahigh dimensional regressions, as it is difficult to specify a correct model for ultrahigh dimensional predictors.Once the assuming data generating process does not meet the actual one, the screening method based on the model will be problematic. We establish the sure screening property and consistency in ranking property of the proposed method. Simulations are conducted to study the finite sample performances, and the results demonstrate that the proposed method is competitive compared with the existing methods. We also illustrate the results via the analysis of data from The National Alzheimers Coordinating Center(NACC).

关 键 词：censored data consistency in ranking PROPERTY FEATURE selection HIGH-DIMENSIONAL data sure SCREENING PROPERTY VARYING COEFFICIENT single-index model 

分 类 号：R195.1[医药卫生—卫生统计学]