Heteroscedastic Variance Components

Heteroscedastic Variance Components is a statistical framework in variance components analysis that explicitly models unequal (heteroscedastic) error variances across different groups or levels of a random effect, thereby distinguishing itself from classical variance components models that assume homogeneity of variance.

318

Publications

33.7K

Citations

618

Authors

355

Institutions

Publications per year

2017–2026

29

Authors

618

  1. RR
    Rand R. Wilcox

    University of Southern California, United States

    5 publications · 214 citations

    5

    h-index

  2. RA
    Roger A. Horn

    Johns Hopkins University, United States

    4 publications · 297 citations

    4

    h-index

  3. SD
    Susan D. Horn

    Outcomes Research Consortium, United States

    4 publications · 297 citations

    4

    h-index

  4. HP
    Hans‐Peter Piepho

    University of Hohenheim, Germany

    4 publications · 73 citations

    4

    h-index

  5. FA
    Franklin A. Graybill

    Colorado State University, United States

    4 publications · 101 citations

    4

    h-index

1–5 of 618

Venues

  1. Journal of the American Statistical Association

    32 publications · 7.9K citations

    22

    h-index

  2. Biometrika

    20 publications · 3.3K citations

    17

    h-index

  3. Biometrics

    13 publications · 1.6K citations

    13

    h-index

  4. Technometrics

    10 publications · 389 citations

    10

    h-index

  5. Journal of Multivariate Analysis

    9 publications · 258 citations

    9

    h-index

1–5