Covariance Matrix Approximation

Covariance Matrix Approximation is a mathematical technique in spatial statistics that belongs to the class of dimensionality reduction and computational optimization methods, distinguished by its focus on constructing a simplified, computationally efficient representation of a high-dimensional spatial covariance matrix while preserving its essential structural and statistical properties for use in spatial prediction and inference.

637

Publications

53.3K

Citations

1.6K

Authors

707

Institutions

Publications per year

2017–2026

149

Authors

1.6K

  1. JF
    Jianqing Fan

    Princeton University, United States

    8 publications · 1.4K citations

    8

    h-index

  2. MG
    Marc G. Genton

    King Abdullah University of Science and Technology, Saudi Arabia

    6 publications · 336 citations

    6

    h-index

  3. AH
    Arnold Heemink

    Delft University of Technology, Netherlands

    5 publications · 204 citations

    5

    h-index

  4. MV
    Martin Verlaan

    Delft University of Technology, Netherlands

    5 publications · 204 citations

    5

    h-index

  5. DE
    David E. Keyes

    King Abdullah University of Science and Technology, Saudi Arabia

    4 publications · 131 citations

    4

    h-index

1–5 of 1.6K

Venues

  1. IEEE Transactions on Signal Processing

    22 publications · 2.5K citations

    17

    h-index

  2. Journal of the American Statistical Association

    16 publications · 2.8K citations

    15

    h-index

  3. Monthly Notices of the Royal Astronomical Society

    15 publications · 1.1K citations

    14

    h-index

  4. Journal of Multivariate Analysis

    13 publications · 404 citations

    12

    h-index

  5. arXiv (Cornell University)

    Repository · 16 publications · 684 citations

    12

    h-index

1–5