Sparse reduced-rank regression for integrating omics data

被引：0
作者：
Haileab Hilafu
Sandra E. Safo
Lillian Haine
机构：
[1] University of Tennessee,Department of Business Analytics and Statistics
[2] University of Minnesota,Division of Biostatistics
来源：
BMC Bioinformatics | / 21卷
关键词：
Integrative analysis; Multi-view data; Reduced rank regression; High dimensional data;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [1] Sparse reduced-rank regression for integrating omics data
    Hilafu, Haileab
    Safo, Sandra E.
    Haine, Lillian
    BMC BIOINFORMATICS, 2020, 21 (01)
  • [2] Sparse reduced-rank regression with covariance estimation
    Lisha Chen
    Jianhua Z. Huang
    Statistics and Computing, 2016, 26 : 461 - 470
  • [3] Sparse reduced-rank regression with covariance estimation
    Chen, Lisha
    Huang, Jianhua Z.
    STATISTICS AND COMPUTING, 2016, 26 (1-2) : 461 - 470
  • [4] Robust Sparse Reduced-Rank Regression with Response Dependency
    Liu, Wenchen
    Liu, Guanfu
    Tang, Yincai
    SYMMETRY-BASEL, 2022, 14 (08):
  • [5] Envelopes and reduced-rank regression
    Cook, R. Dennis
    Forzani, Liliana
    Zhang, Xin
    BIOMETRIKA, 2015, 102 (02) : 439 - 456
  • [6] BIPLOTS IN REDUCED-RANK REGRESSION
    TERBRAAK, CJF
    LOOMAN, CWN
    BIOMETRICAL JOURNAL, 1994, 36 (08) : 983 - 1003
  • [7] Envelope-based sparse reduced-rank regression for multivariate linear model
    Guo, Wenxing
    Balakrishnan, Narayanaswamy
    He, Mu
    JOURNAL OF MULTIVARIATE ANALYSIS, 2023, 195
  • [8] A Poisson reduced-rank regression model for association mapping in sequencing data
    Tiana Fitzgerald
    Andrew Jones
    Barbara E. Engelhardt
    BMC Bioinformatics, 23
  • [9] A Poisson reduced-rank regression model for association mapping in sequencing data
    Fitzgerald, Tiana
    Jones, Andrew
    Engelhardt, Barbara E.
    BMC BIOINFORMATICS, 2022, 23 (01)
  • [10] The maximum likelihood estimate in reduced-rank regression
    Eldén, L
    Savas, B
    NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, 2005, 12 (08) : 731 - 741
12345