LASSO-type variable selection methods for high-dimensional data

被引：1
|
作者：
Fu, Guanghui [1 ]
Wang, Pan [1 ]
机构：
[1] Kunming Univ Sci & Technol, Coll Sci, Kunming 650500, Peoples R China
来源：
ADVANCES IN COMPUTATIONAL MODELING AND SIMULATION, PTS 1 AND 2 | 2014年 / 444-445卷
关键词：
LASSO; Variable selection; High-dimensional data; Oracle property; Group effect; ORACLE PROPERTIES; ELASTIC-NET; REGRESSION; SHRINKAGE;
D O I：
10.4028/www.scientific.net/AMM.0.604
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
LASSO is a very useful variable selection method for high-dimensional data, But it does not possess oracle property[Fan and Li, 2001] and group effect[Zou and Hastie, 2005]. In this paper, we firstly review four improved LASSO-type methods which satisfy oracle property and(or) group effect, and then give another two new ones called WFEN and WFAEN. The performance on both the simulation and real data sets shows that WFEN and WFAEN are competitive with other LASSO-type methods.
引用
收藏
页码：604 / 609
页数：6
相关论文
共 50 条
  • [31] Stochastic variational variable selection for high-dimensional microbiome data
    Dang, Tung
    Kumaishi, Kie
    Usui, Erika
    Kobori, Shungo
    Sato, Takumi
    Toda, Yusuke
    Yamasaki, Yuji
    Tsujimoto, Hisashi
    Ichihashi, Yasunori
    Iwata, Hiroyoshi
    MICROBIOME, 2022, 10 (01)
  • [32] Variable selection and subgroup analysis for high-dimensional censored data
    Zhang, Yu
    Wang, Jiangli
    Zhang, Weiping
    STATISTICAL THEORY AND RELATED FIELDS, 2024, 8 (03) : 211 - 231
  • [33] High-dimensional variable selection in regression and classification with missing data
    Gao, Qi
    Lee, Thomas C. M.
    SIGNAL PROCESSING, 2017, 131 : 1 - 7
  • [34] Scalable Bayesian variable selection for structured high-dimensional data
    Chang, Changgee
    Kundu, Suprateek
    Long, Qi
    BIOMETRICS, 2018, 74 (04) : 1372 - 1382
  • [35] Sparse Bayesian variable selection for classifying high-dimensional data
    Yang, Aijun
    Lian, Heng
    Jiang, Xuejun
    Liu, Pengfei
    STATISTICS AND ITS INTERFACE, 2018, 11 (02) : 385 - 395
  • [36] RANKING-BASED VARIABLE SELECTION FOR HIGH-DIMENSIONAL DATA
    Baranowski, Rafal
    Chen, Yining
    Fryzlewicz, Piotr
    STATISTICA SINICA, 2020, 30 (03) : 1485 - 1516
  • [37] Bayesian variable selection in clustering high-dimensional data with substructure
    Michael D. Swartz
    Qianxing Mo
    Mary E. Murphy
    Joanne R. Lupton
    Nancy D. Turner
    Mee Young Hong
    Marina Vannucci
    Journal of Agricultural, Biological, and Environmental Statistics, 2008, 13 : 407 - 423
  • [38] A Robust Supervised Variable Selection for Noisy High-Dimensional Data
    Kalina, Jan
    Schlenker, Anna
    BIOMED RESEARCH INTERNATIONAL, 2015, 2015
  • [39] Estimation and variable selection for high-dimensional spatial data models
    Hou, Li
    Jin, Baisuo
    Wu, Yuehua
    JOURNAL OF ECONOMETRICS, 2024, 238 (02)
  • [40] Variable selection for longitudinal data with high-dimensional covariates and dropouts
    Zheng, Xueying
    Fu, Bo
    Zhang, Jiajia
    Qin, Guoyou
    JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2018, 88 (04) : 712 - 725
12345