High-dimensional Dependence

High-Dimensional Dependence is a statistical framework that models the complex interdependencies among a large number of random variables, where the dependence structure cannot be adequately captured by pairwise or low-dimensional methods due to the curse of dimensionality and the need for scalable, structured, or sparse representations.

131

Publications

6.6K

Citations

319

Authors

201

Institutions

Publications per year

2017–2026

47

Authors

319

  1. JF
    Jianqing Fan

    Princeton University, United States

    5 publications · 162 citations

    5

    h-index

  2. JC
    Jinyuan Chang

    Southwestern University of Finance and Economics, China

    4 publications · 163 citations

    4

    h-index

  3. CC
    Claudia Czado

    Technical University of Munich, Germany

    4 publications · 316 citations

    4

    h-index

  4. GC
    Guang Cheng

    Purdue University West Lafayette, United States

    3 publications · 124 citations

    3

    h-index

  5. XZ
    Xianyang Zhang

    Texas A&M University, United States

    3 publications · 92 citations

    3

    h-index

1–5 of 319

Venues

  1. arXiv (Cornell University)

    Repository · 18 publications · 487 citations

    12

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 4 publications · 70 citations

    4

    h-index

  3. The Annals of Statistics

    4 publications · 178 citations

    4

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 95 citations

    4

    h-index

  5. 3

    h-index

1–5