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
收藏
页码：212 / 238
页数：27
相关论文
共 50 条
  • [21] Robust and sparse estimation methods for high-dimensional linear and logistic regression
    Kurnaz, Fatma Sevinc
    Hoffmann, Irene
    Filzmoser, Peter
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2018, 172 : 211 - 222
  • [22] High-dimensional inference for linear model with correlated errors
    Yuan, Panxu
    Guo, Xiao
    METRIKA, 2022, 85 (01) : 21 - 52
  • [23] Sparse estimation in high-dimensional linear errors-in-variables regression via a covariate relaxation method
    Li, Xin
    Wu, Dongya
    STATISTICS AND COMPUTING, 2024, 34 (01)
  • [24] Robust Adaptive Lasso method for parameter's estimation and variable selection in high-dimensional sparse models
    Wahid, Abdul
    Khan, Dost Muhammad
    Hussain, Ijaz
    PLOS ONE, 2017, 12 (08):
  • [25] OPTIMAL SPARSE VOLATILITY MATRIX ESTIMATION FOR HIGH-DIMENSIONAL ITO PROCESSES WITH MEASUREMENT ERRORS
    Tao, Minjing
    Wang, Yazhen
    Zhou, Harrison H.
    ANNALS OF STATISTICS, 2013, 41 (04) : 1816 - 1864
  • [26] Monitoring of group-structured high-dimensional processes via sparse group LASSO
    Kim, Sangahn
    Turkoz, Mehmet
    Jeong, Myong K.
    Elsayed, Elsayed A.
    ANNALS OF OPERATIONS RESEARCH, 2024, 340 (2-3) : 891 - 911
  • [27] Partial least square based approaches for high-dimensional linear mixed models
    Bazzoli, Caroline
    Lambert-Lacroix, Sophie
    Martinez, Marie-Jose
    STATISTICAL METHODS AND APPLICATIONS, 2023, 32 (03) : 769 - 786
  • [28] Penalised robust estimators for sparse and high-dimensional linear models
    Amato, Umberto
    Antoniadis, Anestis'
    De Feis, Italia
    Gijbels, Irene
    STATISTICAL METHODS AND APPLICATIONS, 2021, 30 (01) : 1 - 48
  • [29] Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits
    Chakraborty, Sunrit
    Roy, Saptarshi
    Tewari, Ambuj
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 202, 2023, 202
  • [30] Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data
    Wang, Haohan
    Lengerich, Benjamin J.
    Aragam, Bryon
    Xing, Eric P.
    BIOINFORMATICS, 2019, 35 (07) : 1181 - 1187
12345