文献详情
Feature screening for ultrahigh-dimensional additive logistic models
文献类型:期刊
通讯作者:Zhang, JX (reprint author), Renmin Univ China, Ctr Appl Stat, Sch Stat, Beijing 100872, Peoples R China.
期刊名称:JOURNAL OF STATISTICAL PLANNING AND INFERENCE影响因子和分区
年:2020
卷:205
页码:306-317
ISSN:0378-3758
关键词:Feature screening; Additive logistic models; Sure independence screening property; False selection rate
所属部门:统计学院
摘要:This paper introduces a sure screening method for ultrahigh-dimensional additive logistic models. With binary response variable, additive logistic model is very useful in social and biological research such as disease detection. The proposed feature screening procedure, ALNIS (nonparametric independence screening for additive logistic models), employs B-spline approximation to model the marginal effect, transforming nonparametric problems into parametric ones. This screening process ranks the no ...More
This paper introduces a sure screening method for ultrahigh-dimensional additive logistic models. With binary response variable, additive logistic model is very useful in social and biological research such as disease detection. The proposed feature screening procedure, ALNIS (nonparametric independence screening for additive logistic models), employs B-spline approximation to model the marginal effect, transforming nonparametric problems into parametric ones. This screening process ranks the nonparametric components according to their norms of the marginal likelihood estimate. Under appropriate conditions, the proposed method is shown to possess sure screening property with a vanishing false selection rate. In numerical studies, we use simulated data to compare the performance of the proposed approach with other seven methods that allow the existence of binary response. We further illustrate the proposed procedure by a real data analysis. Numerical comparison indicates that the proposed approach enjoys robustness and effectiveness under ultrahigh-dimensional additive logistic models. (C) 2019 Elsevier By. All rights reserved. ...Hide
DOI:10.1016/j.jspi.2019.08.005
百度学术:Feature screening for ultrahigh-dimensional additive logistic models
语言:外文
人气指数:3
浏览次数:3
基金:Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities; Research Funds of Renmin University of China [18XN1010]
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Microcystis aeruginosa Synergistically Facilitate the Photocatalytic Degradation of Tetracycline Hydrochloride and Cr(VI) on PAN/TiO2/Ag Nanofiber Mats.Wang, Lei, Zhang, Changbo, Cheng, Rong, et al. .CATALYSTS. 2018, 8(12).
Machine Learning to Instruct Single Crystal Growth by Flux Method.Yao, Tang-Shi, Tang, Cen-Yao, Yang, Meng, et al. .CHINESE PHYSICS LETTERS. 2019, 36(6).
Regularization methods for high-dimensional sparse control function models.Xu, Xinyi, Li, Xiangjie, Zhang, Jingxiao,.JOURNAL OF STATISTICAL PLANNING AND INFERENCE. 2020, 206, 111-126.
Seeing Is Believing: Experimental Evidence on the Impact of Eyeglasses on Academic Performance, Aspirations, and Dropout among Junior High School Students in Rural China.Nie, Jingchun, Pang, Xiaopeng, Wang, Lei, et al. .ECONOMIC DEVELOPMENT AND CULTURAL CHANGE. 2020, 68(2), 335-355.
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Feature screening for ultrahigh-dimensional additive logistic models
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