Correlation Matrix Estimation

Correlation Matrix Estimation is a statistical method that belongs to the class of multivariate analysis techniques, specifically designed to estimate the pairwise correlation coefficients among multiple random variables from sample data while ensuring the resulting matrix is positive semi-definite and accurately reflects the underlying dependence structure.

1.4K

Publications

118.2K

Citations

2.7K

Authors

1.2K

Institutions

Publications per year

2017–2026

232

Authors

2.7K

  1. JF
    Jianqing Fan

    Princeton University, United States

    8 publications · 763 citations

    8

    h-index

  2. MZ
    M.D. Zoltowski

    Purdue University West Lafayette, United States

    7 publications · 220 citations

    7

    h-index

  3. CC
    Christophe Croux

    KU Leuven, Belgium

    7 publications · 647 citations

    7

    h-index

  4. MV
    Mats Viberg

    Chalmers University of Technology, Sweden

    6 publications · 256 citations

    6

    h-index

  5. JR
    James R. Schott

    University of Central Florida, United States

    5 publications · 103 citations

    5

    h-index

1–5 of 2.7K

Venues

  1. Biometrika

    64 publications · 8.3K citations

    36

    h-index

  2. Psychometrika

    36 publications · 8.2K citations

    26

    h-index

  3. Journal of the American Statistical Association

    36 publications · 7.5K citations

    24

    h-index

  4. IEEE Transactions on Signal Processing

    32 publications · 1.2K citations

    22

    h-index

  5. Multivariate Behavioral Research

    MBR · 29 publications · 3.8K citations

    21

    h-index

1–5