Covariance Decomposition

Covariance Decomposition is a mathematical framework within spatial covariance modeling that decomposes the overall covariance structure of spatially referenced data into additive or multiplicative components representing distinct sources of spatial dependence, such as local and global effects, anisotropy, or scale-specific variability.

73

Publications

4.3K

Citations

215

Authors

120

Institutions

Publications per year

2017–2026

17

Authors

215

  1. KV
    Kanti V. Mardia

    University of Leeds, United Kingdom

    2 publications · 83 citations

    2

    h-index

  2. HR
    Herschel Rabitz

    Princeton University, United States

    2 publications · 252 citations

    2

    h-index

  3. SJ
    Stephen Jay Gould

    Harvard University, United States

    2 publications · 108 citations

    2

    h-index

  4. SB
    Simon B. Eickhoff

    Heinrich Heine University Düsseldorf, Germany

    2 publications · 103 citations

    2

    h-index

  5. YC
    Yves Caniou

    Laboratoire de l'Informatique du Parallélisme, France

    1 publications · 13 citations

    1

    h-index

1–5 of 215

Venues

  1. NeuroImage

    4 publications · 340 citations

    4

    h-index

  2. Journal of Multivariate Analysis

    3 publications · 117 citations

    3

    h-index

  3. Mathematical Geology

    2 publications · 83 citations

    2

    h-index

  4. IEEE Transactions on Signal Processing

    2 publications · 738 citations

    2

    h-index

  5. 2

    h-index

1–5