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Robust Signal Recovery for High-Dimensional Linear Log-Contrast Models with Compositional Covariates

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成果类型:
期刊论文
作者:
Han, Dongxiao;Huang, Jian;Lin, Yuanyuan;Liu, Lei;Qu, Lianqiang;...
通讯作者:
Yuanyuan Lin
作者机构:
[Han, Dongxiao] Nankai Univ, Sch Stat & Data Sci, LPMC, KLMDASR, Tianjin, Peoples R China.
[Han, Dongxiao] Nankai Univ, LEBPS, Tianjin, Peoples R China.
[Huang, Jian] Univ Iowa, Dept Stat & Actuarial Sci, Iowa City, IA 52242 USA.
[Lin, Yuanyuan] Chinese Univ Hong Kong, Dept Stat, Shatin, Hong Kong, Peoples R China.
[Liu, Lei] Washington Univ, Div Biostat, St Louis, MO 63110 USA.
通讯机构:
[Yuanyuan Lin] D
Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
语种:
英文
关键词:
Compositional data;Consistent estimation;Huber loss;Lasso;Support recovery
期刊:
Journal of Business & Economic Statistics
ISSN:
0735-0015
年:
2023
卷:
41
期:
3
页码:
957-967
基金类别:
National Natural Science Foundation of China [12001219, 12171463, 12101330, 11961028]; Fundamental Research Funds for the Central Universities, Nankai University [9920200110]; U.S. National Science Foundation [DMS-1916199]; Hong Kong Research Grants Council [14306219, 14306620]; Chinese University of Hong Kong; NIH [ULI TR002345]
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:
In this article, we propose a robust signal recovery method for high-dimensional linear log-contrast models, when the error distribution could be heavy-tailed and asymmetric. The proposed method is built on the Huber loss with ℓ1 ℓ1 penalization. We establish the ℓ1 ℓ1 and ℓ2 ℓ2 consistency for the resulting estimator. Under conditions analogous to the irrepresentability condition and the minimum signal strength condition, we prove that the signed support of the slope parameter vector can be recovered with high probability. The finite-sam...

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