Mixed-effects Model Development

Mixed-Effects Model Development is a methodological contribution in statistical modeling that integrates both fixed and random effects to account for population-level and group-level variations in data, distinguishing itself by enabling inference on hierarchical or longitudinal structures while preserving efficiency and interpretability.

88

Publications

6.5K

Citations

323

Authors

172

Institutions

Publications per year

2017–2026

29

Authors

323

  1. XL
    Xiangdong Lei

    Institute of Forest Resource Information Techniques, China

    2 publications · 81 citations

    2

    h-index

  2. KE
    Keith E. Muller

    University of North Carolina at Chapel Hill, United States

    2 publications · 283 citations

    2

    h-index

  3. KA
    Kelli A. Komro

    University of Minnesota, United States

    2 publications · 283 citations

    2

    h-index

  4. LJ
    Lloyd J. Edwards

    University of North Carolina at Chapel Hill, United States

    2 publications · 283 citations

    2

    h-index

  5. WZ
    Weisheng Zeng

    State Forestry and Grassland Administration, China

    2 publications · 106 citations

    2

    h-index

1–5 of 323

Venues

  1. Forest Science

    3 publications · 80 citations

    3

    h-index

  2. PLoS ONE

    3 publications · 142 citations

    3

    h-index

  3. Statistics in Medicine

    2 publications · 324 citations

    2

    h-index

  4. Advances in Methods and Practices in Psychological Science

    AMPPS · 2 publications · 707 citations

    2

    h-index

  5. Biostatistics

    2 publications · 123 citations

    2

    h-index

1–5