The rate of convergence for sparse and low-rank quantile trace regression

被引：3
|
作者：
Tan, Xiangyong [1 ,2 ]
Peng, Ling [1 ,2 ]
Xiao, Peiwen [1 ,2 ]
Liu, Qing [1 ,2 ]
Liu, Xiaohui [1 ,2 ]
机构：
[1] Jiangxi Univ Finance & Econ, Sch Stat & Data Sci, Nanchang 330013, Jiangxi, Peoples R China
[2] Jiangxi Univ Finance & Econ, Key Lab Data Sci Finance & Econ, Nanchang 330013, Jiangxi, Peoples R China
来源：
JOURNAL OF COMPLEXITY | 2023年 / 79卷
关键词：
Low rank; Matrix covariates; Convergence rate; Quantile trace regression; Row (column) sparsity;
D O I：
10.1016/j.jco.2023.101778
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Trace regression models are widely used in applications involving panel data, images, genomic microarrays, etc., where high dimensional covariates are often involved. However, the existing research involving high-dimensional covariates focuses mainly on the condition mean model. In this paper, we extend the trace regression model to the quantile trace regression model when the parameter is a matrix of simultaneously low rank and row (column) sparsity. The convergence rate of the penalized estimator is derived under mild conditions. Simulations, as well as a real data application, are also carried out for illustration.
引用
收藏
页数：19
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