Bootstrap Estimation
Bootstrap Estimation is a resampling technique that belongs to the family of statistical inference methods, distinguished by its approach of repeatedly sampling with replacement from the observed data to estimate the sampling distribution of a statistic, thereby enabling robust assessment of model variability and uncertainty.
2.1K
Publications
239.7K
Citations
3.2K
Authors
1.4K
Institutions
Publications per year
2017–2026
316
Parents
2.1K
Bootstrap Methods: Another Look at the Jackknife
B. Efron · The Annals of Statistics · 1979 · 17.4K citations · Full text
UFBoot2: Improving the Ultrafast Bootstrap Approximation
Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler et al. · Molecular Biology and Evolution · 2017 · 11K citations · Full text
Estimation and Inference in Econometrics.
Marcus J. Chambers, Russell Davidson, James G. MacKinnon · The Economic Journal · 1994 · 5.6K citations
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University of California San Diego, United States
24 publications · 5.8K citations
18
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17
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1.4K

Australian National University
Canberra, Australia
35 h-index
92 publications
5.5K citations
Stanford University
Stanford, United States
33 h-index
63 publications
37.1K citations
University of California, Berkeley
Berkeley, United States
27 h-index
59 publications
7.7K citations
University of Wisconsin–Madison
Madison, United States
24 h-index
49 publications
4.9K citations
University of California San Diego
San Diego, United States
22 h-index
51 publications
6.2K citations
Queen's University
Kingston, Canada
22 h-index
34 publications
5K citations
University of Michigan
Ann Arbor, United States
21 h-index
42 publications
3.5K citations
Texas A&M University
College Station, United States
21 h-index
42 publications
5.5K citations
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131 publications · 36.3K citations
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120 publications · 36.6K citations
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45 publications · 3.8K citations
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50 publications · 4.8K citations
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CSDA · 46 publications · 2.8K citations
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