Mixed Model Variance Decomposition

Mixed Model Variance Decomposition is a statistical method that decomposes error variance in hierarchical or longitudinal data by distinguishing between fixed and random effects to quantify the proportion of variability attributable to each level within a mixed-effects modeling framework.

10

Publications

1.4K

Citations

11

Authors

10

Institutions

Publications per year

2017–2026

4

Authors

11

  1. HS
    Holger Schielzeth

    Friedrich Schiller University Jena, Germany

    4 publications · 800 citations

    4

    h-index

  2. SN
    Shinichi Nakagawa

    UNSW Sydney, Australia

    4 publications · 800 citations

    4

    h-index

  3. MA
    Martin A. Stoffel

    University of Edinburgh, United Kingdom

    3 publications · 409 citations

    3

    h-index

  4. BB
    Babette Brumback

    University of Florida, United States

    2 publications · 514 citations

    2

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  5. AA
    Arthur Albert

    Boston University, United States

    1 publications · 15 citations

    1

    h-index

1–5 of 11

Venues

  1. 2

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  2. The Annals of Statistics

    2 publications · 33 citations

    2

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  3. 1

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  4. Methods in Ecology and Evolution

    1 publications · 391 citations

    1

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  5. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 1 publications · 37 citations

    1

    h-index

1–5