Dependency Structure Modeling

Dependency Structure Modeling is a statistical framework within multilevel meta-analysis that specifies the hierarchical and correlated nature of effect sizes by formally representing the nested or cross-classified relationships among effect size estimates, thereby distinguishing it from conventional meta-analytic models that assume independence.

333

Publications

17.8K

Citations

863

Authors

373

Institutions

Publications per year

2017–2026

87

Authors

863

  1. TY
    Tian–Li Yu

    University of Illinois Urbana-Champaign, United States

    7 publications · 265 citations

    7

    h-index

  2. DE
    David E. Goldberg

    University of Illinois Urbana-Champaign, United States

    5 publications · 171 citations

    5

    h-index

  3. PS
    Philipp Schönbucher

    ETH Zurich, Switzerland

    5 publications · 466 citations

    5

    h-index

  4. YM
    Yūji Matsumoto

    Nara Institute of Science and Technology, Japan

    5 publications · 228 citations

    5

    h-index

  5. AR
    Alexander Ratner

    Stanford University, United States

    4 publications · 265 citations

    4

    h-index

1–5 of 863

Venues

  1. arXiv (Cornell University)

    Repository · 23 publications · 866 citations

    16

    h-index

  2. SSRN Electronic Journal

    Repository · 5 publications · 416 citations

    5

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 231 citations

    4

    h-index

  4. DSpace@MIT (Massachusetts Institute of Technology)

    Repository · 4 publications · 165 citations

    4

    h-index

  5. Bioinformatics

    3 publications · 375 citations

    3

    h-index

1–5