Point Process Approach

The "Point Process Approach" is a stochastic modeling framework within extreme value theory that characterizes the occurrence of extreme events in continuous space or time by modeling their locations as a point process, distinguished from alternative methods by its ability to jointly model the frequency, magnitude, and spatial or temporal clustering of extremes.

176

Publications

11.4K

Citations

374

Authors

226

Institutions

Publications per year

2017–2026

38

Authors

374

  1. HZ
    Hongyuan Zha

    Georgia Institute of Technology, United States

    6 publications · 271 citations

    6

    h-index

  2. EN
    Emery N. Brown

    Massachusetts General Hospital, United States

    5 publications · 1.8K citations

    5

    h-index

  3. DR
    David R. Brillinger

    University of California, Berkeley, United States

    4 publications · 75 citations

    4

    h-index

  4. LS
    Le Song

    Georgia Institute of Technology, United States

    4 publications · 129 citations

    4

    h-index

  5. SI
    Sidney I. Resnick

    Cornell University, United States

    3 publications · 445 citations

    3

    h-index

1–5 of 374

Venues

  1. Advances in Applied Probability

    AAP · 11 publications · 995 citations

    11

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 135 citations

    6

    h-index

  3. Scandinavian Journal of Statistics

    3 publications · 99 citations

    3

    h-index

  4. Environmetrics

    3 publications · 70 citations

    3

    h-index

  5. 3

    h-index

1–5