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Statistical Theory and Related Fields Volume 4, 2020 - Issue 1
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Group screening for ultra-high-dimensional feature under linear model

Yong Niua School of Statistics, East China Normal University, Shanghai, People's Republic of China;b Department of Mathematics and Physics, Hefei University, Hefei, People's Republic of ChinaView further author information
,
Riquan Zhanga School of Statistics, East China Normal University, Shanghai, People's Republic of ChinaCorrespondence[email protected]
[email protected]
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,
Jicai Liuc Department of Mathematics, Shanghai Normal University, Shanghai, People's Republic of ChinaView further author information
&
Huapeng Lia School of Statistics, East China Normal University, Shanghai, People's Republic of ChinaView further author information
Pages 43-54 | Received 18 Jul 2018, Accepted 17 Jun 2019, Published online: 04 Jul 2019

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Read on this site (2)

Hanji He & Guangming Deng. (2022) Grouped feature screening for ultra-high dimensional data for the classification model. Journal of Statistical Computation and Simulation 92:5, pages 974-997.
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Lei Huo, Xuerong Meggie Wen & Zhou Yu. (2020) A model-free conditional screening approach via sufficient dimension reduction. Journal of Nonparametric Statistics 32:4, pages 970-988.
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Articles from other publishers (3)

Zhongzheng Wang, Guangming Deng & Haiyun Xu. (2023) Group feature screening based on Gini impurity for ultrahigh-dimensional multi-classification. AIMS Mathematics 8:2, pages 4342-4362.
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Zhongzheng Wang, Guangming Deng & Jianqi Yu. (2022) Group Feature Screening Based on Information Gain Ratio for Ultrahigh-Dimensional Data. Journal of Mathematics 2022, pages 1-15.
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Jicai Liu, Yuefeng Si, Yong Niu & Riquan Zhang. (2022) Projection quantile correlation and its use in high-dimensional grouped variable screening. Computational Statistics & Data Analysis 167, pages 107369.
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