Residual Bootstrap
Residual Bootstrap is a resampling technique in statistical inference where the bootstrap samples are constructed by resampling the residuals of a fitted model and adding them back to the predicted values to generate new response variables, thereby preserving the explanatory variables' structure while accounting for model uncertainty.
74
Publications
5.3K
Citations
176
Authors
111
Institutions
Publications per year
2017–2026
12
Variants
Residual Bootstrapping
Parents
74
1–5 of 74
176
5
h-index
3
h-index
Universidade de Santiago de Compostela, Spain
3 publications · 361 citations
3
h-index
3
h-index
3
h-index
1–5 of 176
111
Université de Montréal
Montreal, Canada
5 h-index
9 publications
1K citations
Center for Interuniversity Research and Analysis on Organizations
Montreal, Canada
4 h-index
8 publications
955 citations
University of Cyprus
Nicosia, Cyprus
4 h-index
7 publications
374 citations

University of Minnesota
Minneapolis, United States
3 h-index
8 publications
170 citations
University of California, Berkeley
Berkeley, United States
3 h-index
3 publications
210 citations

McGill University
Montreal, Canada
3 h-index
6 publications
133 citations
Center for Economic and Policy Research
Washington D.C., United States
2 h-index
3 publications
776 citations
Texas A&M University
College Station, United States
2 h-index
4 publications
264 citations
1–8 of 111
4 publications · 698 citations
4
h-index
3 publications · 66 citations
3
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3 publications · 84 citations
3
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2 publications · 62 citations
2
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2 publications · 259 citations
2
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1–5