Sparse Correlation Modeling

Sparse Correlation Modeling is a statistical framework that belongs to the class of multivariate association methods, distinguished by its imposition of sparsity constraints to identify and estimate only the most significant pairwise correlations among high-dimensional omics variables while shrinking negligible associations to zero.

70

Publications

4.2K

Citations

517

Authors

193

Institutions

Publications per year

2017–2026

29

Authors

517

  1. JY
    Jingwen Yan

    Indiana University – Purdue University Indianapolis, United States

    5 publications · 236 citations

    5

    h-index

  2. AJ
    Andrew J. Saykin

    Indiana University – Purdue University Indianapolis, United States

    5 publications · 236 citations

    5

    h-index

  3. LS
    Li Shen

    Indiana University – Purdue University Indianapolis, United States

    5 publications · 236 citations

    5

    h-index

  4. SL
    Shannon L. Risacher

    Indiana University – Purdue University Indianapolis, United States

    4 publications · 208 citations

    4

    h-index

  5. BA
    Brian Avants

    University of Pennsylvania, United States

    2 publications · 222 citations

    2

    h-index

1–5 of 517

Venues

  1. Bioinformatics

    3 publications · 192 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 67 citations

    3

    h-index

  3. The Annals of Statistics

    3 publications · 56 citations

    3

    h-index

  4. Journal of Computational and Graphical Statistics

    JCGS · 2 publications · 64 citations

    2

    h-index

  5. Biometrics

    2 publications · 74 citations

    2

    h-index

1–5