Robust Mixed-effects Models

Robust Mixed-Effects Models is a statistical modeling framework that extends conventional mixed-effects models by incorporating robust estimation procedures to minimize the influence of outliers and violations of distributional assumptions while accounting for both fixed and random effects in hierarchical or longitudinal data structures.

104

Publications

15.3K

Citations

277

Authors

195

Institutions

Publications per year

2017–2026

26

Authors

277

  1. TL
    Tsung‐I Lin

    National Chung Hsing University, Taiwan

    4 publications · 189 citations

    4

    h-index

  2. RJ
    Reed J D Sorensen

    Institute for Health Metrics and Evaluation, United States

    3 publications · 173 citations

    3

    h-index

  3. PZ
    Peng Zheng

    University of Washington, United States

    3 publications · 173 citations

    3

    h-index

  4. CJ
    Christopher J L Murray

    Institute for Health Metrics and Evaluation, United States

    3 publications · 173 citations

    3

    h-index

  5. AY
    Aleksandr Y. Aravkin

    University of Washington, United States

    3 publications · 173 citations

    3

    h-index

1–5 of 277

Venues

  1. Statistics in Medicine

    6 publications · 230 citations

    6

    h-index

  2. Journal of Statistical Planning and Inference

    5 publications · 274 citations

    5

    h-index

  3. Technometrics

    4 publications · 267 citations

    4

    h-index

  4. Journal of Statistical Software

    JSS · 3 publications · 771 citations

    3

    h-index

  5. 3

    h-index

1–5