Multiple Change-point Detection

Multiple Change-Point Detection is a statistical inference method that identifies more than one point in an ordered sequence of data where abrupt changes occur in the underlying generative process, distinguishing itself from single change-point detection by enabling the simultaneous localization of multiple such transitions.

199

Publications

14.2K

Citations

396

Authors

276

Institutions

Publications per year

2017–2026

67

Authors

396

  1. PF
    Piotr Fryźlewicz

    London School of Economics and Political Science, United Kingdom

    10 publications · 517 citations

    10

    h-index

  2. ML
    Marc Lavielle

    Université Paris-Sud, France

    5 publications · 623 citations

    5

    h-index

  3. MA
    Marc A. Suchard

    University of California, Los Angeles, United States

    5 publications · 404 citations

    5

    h-index

  4. NA
    Nicholas A. James

    Cornell University, United States

    4 publications · 839 citations

    4

    h-index

  5. HC
    Haeran Cho

    University of Bristol, United Kingdom

    4 publications · 272 citations

    4

    h-index

1–5 of 396

Venues

  1. arXiv (Cornell University)

    Repository · 14 publications · 741 citations

    12

    h-index

  2. The Annals of Statistics

    7 publications · 1.2K citations

    7

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 6 publications · 238 citations

    6

    h-index

  4. Journal of Statistical Software

    JSS · 3 publications · 1.7K citations

    4

    h-index

  5. Sequential Analysis

    4 publications · 132 citations

    4

    h-index

1–5