Spatial Change-point Detection

Spatial Change-Point Detection is a statistical methodology within Bayesian spatial analysis that identifies abrupt changes in the spatial pattern or structure of a process by modeling the location and nature of discontinuities as latent change-points in a spatially referenced dataset.

66

Publications

2.4K

Citations

159

Authors

113

Institutions

Publications per year

2017–2026

21

Authors

159

  1. BB
    Bernard Bobée

    Institut National de la Recherche Scientifique, Canada

    2 publications · 235 citations

    2

    h-index

  2. RW
    Rebecca Willett

    Duke University, United States

    2 publications · 43 citations

    2

    h-index

  3. WH
    William Herlands

    Carnegie Mellon University, United States

    2 publications · 30 citations

    2

    h-index

  4. LP
    Luc Perreault

    Hydro-Québec, Canada

    2 publications · 235 citations

    2

    h-index

  5. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    2 publications · 30 citations

    2

    h-index

1–5 of 159

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 129 citations

    5

    h-index

  2. Journal of Multivariate Analysis

    3 publications · 56 citations

    3

    h-index

  3. 2

    h-index

  4. Environmetrics

    2 publications · 28 citations

    2

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 2 publications · 45 citations

    2

    h-index

1–5