Self-exciting Processes

Self-Exciting Processes is a type of counting process in which the occurrence of each event increases the likelihood of subsequent events, distinguished by an intensity function that depends on the history of past events, thereby modeling phenomena where activity fosters further activity.

191

Publications

18.1K

Citations

381

Authors

242

Institutions

Publications per year

2017–2026

71

Variants

Self-exciting Counting Processes

Authors

381

  1. AL
    Andrea L. Bertozzi

    University of California, Los Angeles, United States

    6 publications · 468 citations

    6

    h-index

  2. KY
    Kai Yang

    Changchun University of Technology

    5 publications · 149 citations

    5

    h-index

  3. PJ
    P. Jeffrey Brantingham

    University of California, Los Angeles, United States

    5 publications · 1.1K citations

    5

    h-index

  4. FC
    Feng Chen

    UNSW Sydney, Australia

    4 publications · 116 citations

    4

    h-index

  5. AG
    Alan G. Hawkes

    Swansea University, United Kingdom

    4 publications · 3.3K citations

    4

    h-index

1–5 of 381

Venues

  1. Journal of Applied Probability

    JAP · 10 publications · 1.6K citations

    10

    h-index

  2. arXiv (Cornell University)

    Repository · 8 publications · 247 citations

    8

    h-index

  3. Quantitative Finance

    7 publications · 159 citations

    7

    h-index

  4. SSRN Electronic Journal

    Repository · 6 publications · 517 citations

    6

    h-index

  5. Physical Review E

    PRE · 5 publications · 157 citations

    5

    h-index

1–5