Robust Linear Models is a statistical modeling framework that generalizes standard linear regression by incorporating methods resistant to outliers and violations of classical assumptions, thereby providing reliable parameter estimates under data contamination or heavy-tailed error distributions.
598
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66.2K
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1.2K
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617
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Publications per year
2017–2026
133
Parents
Publications
598
Least Median of Squares Regression
Peter J. Rousseeuw · Journal of the American Statistical Association · 1984 · 3.6K citations
1–5 of 598
Authors
1.2K
- RJ
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1–5 of 1.2K
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617
University of British Columbia
Vancouver, Canada
13 h-index
25 publications
1.6K citations
KU Leuven
Leuven, Belgium
10 h-index
17 publications
1.3K citations
University of Alberta
Edmonton, Canada
10 h-index
11 publications
250 citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
10 h-index
16 publications
688 citations
University of California, Berkeley
Berkeley, United States
9 h-index
19 publications
920 citations
Universidad de Buenos Aires
Buenos Aires, Argentina
9 h-index
14 publications
484 citations
Western Michigan University
Kalamazoo, United States
8 h-index
12 publications
817 citations
Pennsylvania State University
State College, United States
8 h-index
8 publications
279 citations
1–8 of 617
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- Technometrics
31 publications · 2.5K citations
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- Journal of the American Statistical Association
31 publications · 7.5K citations
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- The Annals of Statistics
21 publications · 3.2K citations
19
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- Journal of Statistical Planning and Inference
23 publications · 816 citations
15
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- Computational Statistics & Data Analysis
CSDA · 19 publications · 558 citations
15
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