Sequential Change-point Detection

Sequential Change-Point Detection is a statistical inference method that sequentially tests observed data streams to identify the point at which the underlying distribution of a stochastic process undergoes an abrupt change, distinguishing it from other sequential analysis techniques by its focus on detecting distributional shifts in real time.

197

Publications

10.8K

Citations

365

Authors

211

Institutions

Publications per year

2017–2026

48

Authors

365

  1. AG
    Alexander G. Tartakovsky

    University of Southern California, United States

    11 publications · 896 citations

    11

    h-index

  2. YX
    Yao Xie

    Georgia Institute of Technology, United States

    8 publications · 182 citations

    8

    h-index

  3. MP
    Moshe Pollak

    Hebrew University of Jerusalem, Israel

    7 publications · 300 citations

    7

    h-index

  4. GV
    George V. Moustakides

    University of Patras, Greece

    6 publications · 208 citations

    6

    h-index

  5. DS
    David Siegmund

    Stanford University, United States

    5 publications · 478 citations

    5

    h-index

1–5 of 365

Venues

  1. The Annals of Statistics

    14 publications · 1.1K citations

    14

    h-index

  2. Sequential Analysis

    14 publications · 502 citations

    14

    h-index

  3. IEEE Transactions on Information Theory

    9 publications · 297 citations

    9

    h-index

  4. IEEE Transactions on Signal Processing

    7 publications · 394 citations

    7

    h-index

  5. arXiv (Cornell University)

    Repository · 6 publications · 81 citations

    6

    h-index

1–5