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
Parents
104
1–5 of 104
277
4
h-index
Institute for Health Metrics and Evaluation, United States
3 publications · 173 citations
3
h-index
3
h-index
Institute for Health Metrics and Evaluation, United States
3 publications · 173 citations
3
h-index
3
h-index
1–5 of 277
195

National Chung Hsing University
Taichung, Taiwan
4 h-index
5 publications
280 citations
University of South Carolina
Columbia, United States
3 h-index
7 publications
1K citations
University of Wisconsin–Madison
Madison, United States
3 h-index
3 publications
171 citations
Institute for Health Metrics and Evaluation
Seattle, United States
3 h-index
15 publications
865 citations
University of Washington
Seattle, United States
3 h-index
11 publications
807 citations

Universidad de Oviedo
Oviedo, Spain
2 h-index
3 publications
73 citations
University College London
London, United Kingdom
2 h-index
10 publications
303 citations
Universidade de São Paulo
São Paulo, Brazil
2 h-index
2 publications
74 citations
1–8 of 195
6 publications · 230 citations
6
h-index
5 publications · 274 citations
5
h-index
4 publications · 267 citations
4
h-index
JSS · 3 publications · 771 citations
3
h-index
3 publications · 124 citations
3
h-index
1–5