Envelope-based sparse reduced-rank regression for multivariate linear model

被引:1
|
作者
Guo, Wenxing [1 ]
Balakrishnan, Narayanaswamy [2 ]
He, Mu [3 ]
机构
[1] Univ Essex, Dept Math Sci, Colchester, Essex, England
[2] McMaster Univ, Dept Math & Stat, Hamilton, ON, Canada
[3] Xian Jiaotong Liverpool Univ, Dept Fdn Math, Suzhou, Peoples R China
关键词
Dimension reduction; Envelope model; High dimension; Reduced-rank regression; Variable selection; SIMULTANEOUS DIMENSION REDUCTION; SELECTION; ESTIMATOR;
D O I
10.1016/j.jmva.2023.105159
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Envelope models were first proposed by Cook et al. (2010) as a method to reduce estimative and predictive variations in multivariate regression. Sparse reduced-rank regression, introduced by Chen and Huang (2012), is a widely used technique that performs dimension reduction and variable selection simultaneously in multivariate regression. In this work, we combine envelope models and sparse reduced-rank regression method to propose an envelope-based sparse reduced-rank regression estimator, and then establish its consistency, asymptotic normality and oracle property in highdimensional data. We carry out some Monte Carlo simulation studies and also analyze two datasets to demonstrate that the proposed envelope-based sparse reduced-rank regression method displays good variable selection and prediction performance.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] Srrr-cluster: Using Sparse Reduced-Rank Regression to Optimize iCluster
    Ge, Shu-Guang
    Xia, Jun-Feng
    Wei, Pi-Jing
    Zheng, Chun-Hou
    INTELLIGENT COMPUTING METHODOLOGIES, ICIC 2016, PT III, 2016, 9773 : 99 - 106
  • [42] Bayesian Partial Reduced-Rank Regression
    Pintado, Maria F.
    Iacopini, Matteo
    Rossini, Luca
    Shestopaloff, Alexander Y.
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2025,
  • [43] Poisson reduced-rank models with sparse loadings
    Eun Ryung Lee
    Seyoung Park
    Journal of the Korean Statistical Society, 2021, 50 : 1079 - 1097
  • [44] Sparse envelope model: efficient estimation and response variable selection in multivariate linear regression
    Su, Z.
    Zhu, G.
    Chen, X.
    Yang, Y.
    BIOMETRIKA, 2016, 103 (03) : 579 - 593
  • [45] Robust reduced-rank modeling via rank regression
    Zhao, Weihua
    Lian, Heng
    Ma, Shujie
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2017, 180 : 1 - 12
  • [46] Poisson reduced-rank models with sparse loadings
    Lee, Eun Ryung
    Park, Seyoung
    JOURNAL OF THE KOREAN STATISTICAL SOCIETY, 2021, 50 (04) : 1079 - 1097
  • [47] Model-Based Reduced-Rank Pansharpening
    Palsson, Frosti
    Ulfarsson, Magnus O.
    Sveinsson, Johannes R.
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2020, 17 (04) : 656 - 660
  • [48] Speech Emotion Recognition Based on Kernel Reduced-rank Regression
    Zheng, Wenming
    Zhou, Xiaoyan
    2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), 2012, : 1972 - 1976
  • [49] Reduced rank regression with matrix projections for high-dimensional multivariate linear regression model
    Guo, Wenxing
    Balakrishnan, Narayanaswamy
    Bian, Mengjie
    ELECTRONIC JOURNAL OF STATISTICS, 2021, 15 (02): : 4167 - 4191
  • [50] Reduced-rank regularized multivariate model for high-dimensional data
    Kustra, Rafal
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2006, 15 (02) : 312 - 338