Covariance Matrix Estimation

Covariance Matrix Estimation is a statistical method in multivariate analysis that involves the systematic determination of the matrix of pairwise covariances among multiple random variables based on sample data, particularly within the framework of Selection Index Theory where accurate estimation of genetic or phenotypic relationships is essential for index construction and prediction.

4.4K

Publications

446.9K

Citations

8.5K

Authors

2.4K

Institutions

Publications per year

2017–2026

858

Authors

8.5K

  1. PS
    Petre Stoica

    Uppsala University, Sweden

    46 publications · 9K citations

    32

    h-index

  2. AD
    Antonio De Maio

    University of Naples Federico II, Italy

    27 publications · 2.1K citations

    23

    h-index

  3. OL
    Olivier Ledoit

    University of Zurich, Switzerland

    22 publications · 6.7K citations

    19

    h-index

  4. MW
    Michael Wolf

    University of Zurich, Switzerland

    20 publications · 6.6K citations

    18

    h-index

  5. BF
    B. Friedlander

    University of California, Davis, United States

    20 publications · 1.8K citations

    17

    h-index

1–5 of 8.5K

Venues

  1. IEEE Transactions on Signal Processing

    231 publications · 17.2K citations

    71

    h-index

  2. Journal of the American Statistical Association

    176 publications · 19.2K citations

    67

    h-index

  3. The Annals of Statistics

    127 publications · 17.1K citations

    66

    h-index

  4. Biometrika

    90 publications · 6.6K citations

    44

    h-index

  5. 43

    h-index

1–5