Random Effects Selection

Random Effects Selection is a model selection procedure that identifies the appropriate set of random effects in a mixed-effects model by distinguishing between fixed and random sources of variability across groups or clusters in hierarchical data structures.

123

Publications

7K

Citations

298

Authors

177

Institutions

Publications per year

2017–2026

19

Authors

298

  1. JG
    John Gibson

    University of Waikato, New Zealand

    4 publications · 670 citations

    4

    h-index

  2. SS
    Steven Stillman

    Motu Economic and Public Policy Research, New Zealand

    4 publications · 670 citations

    4

    h-index

  3. DM
    David McKenzie

    World Bank Group, United States

    4 publications · 670 citations

    4

    h-index

  4. RK
    R. K. Johnson

    University of Nebraska–Lincoln, United States

    3 publications · 190 citations

    3

    h-index

  5. DB
    David B. Dunson

    Duke University, United States

    3 publications · 435 citations

    3

    h-index

1–5 of 298

Venues

  1. Journal of Animal Science

    9 publications · 441 citations

    9

    h-index

  2. PubMed

    Repository · 5 publications · 80 citations

    5

    h-index

  3. Biometrics

    4 publications · 592 citations

    4

    h-index

  4. Genetics

    3 publications · 81 citations

    3

    h-index

  5. 2

    h-index

1–5