Square-Root LASSO for High-Dimensional Sparse Linear Systems with Weakly Dependent Errors

被引：5

作者：

Xie, Fang ^{[1
,2
]}

Xiao, Zhijie ^{[3
,4
]}

机构：

[1] Univ Macau, Fac Sci & Technol, Dept Math, Taipa, Macao, Peoples R China

[2] UM Zhuhai Res Inst, Zhuhai, Peoples R China

[3] Boston Coll, Dept Econ, 140 Commonwealth Ave, Chestnut Hill, MA 02467 USA

[4] Shandong Univ, Ctr Econ Res, Jinan, Shandong, Peoples R China

来源：

JOURNAL OF TIME SERIES ANALYSIS | 2018年 / 39卷 / 02期

关键词：

high-dimensional linear model; square-root LASSO; -mixing; phi-mixing; m-dependent; estimation consistency; CENTRAL-LIMIT-THEOREM; REGRESSION; CONVERGENCE; PREDICTORS; SHRINKAGE; VARIABLES; SELECTION; RECOVERY; MODELS;

D O I：

10.1111/jtsa.12278

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

We study the square-root LASSO method for high-dimensional sparse linear models with weakly dependent errors. The asymptotic and non-asymptotic bounds for the estimation errors are derived. Our results cover a wide range of weakly dependent errors, including -mixing, -mixing, phi-mixing, and m-dependent types. Numerical simulations are conducted to show the consistency property of square-root LASSO. An empirical application to financial data highlights the importance of the results and method.

引用

页码：212 / 238

页数：27

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