Bootstrap For High-dimensional Data
Bootstrap for High-Dimensional Data is a statistical methodology within the class of bootstrap resampling techniques that specifically addresses the challenges of inference and uncertainty quantification in settings where the number of variables or parameters is comparable to or exceeds the sample size, by adapting resampling strategies to account for high-dimensional asymptotics and structural constraints.
52
Publications
3.3K
Citations
93
Authors
70
Institutions
Publications per year
2017–2026
25
Parents
52
Bootstrap and Wild Bootstrap for High Dimensional Linear Models
Enno Mammen · The Annals of Statistics · 1993 · 895 citations · Full text
Change-point detection in panel data via double CUSUM statistic
Haeran Cho · Electronic Journal of Statistics · 2016 · 146 citations · Full text
1–5 of 52
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Massachusetts Institute of Technology, United States
5 publications · 161 citations
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3
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University of Illinois Urbana-Champaign
Urbana, United States
6 h-index
7 publications
286 citations

Cornell University
Ithaca, United States
5 h-index
7 publications
213 citations
University of California, Davis
Davis, United States
3 h-index
4 publications
160 citations
The University of Tokyo
Tokyo, Japan
3 h-index
2 publications
81 citations

Australian National University
Canberra, Australia
2 h-index
3 publications
898 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
4 publications
318 citations
University of California, Los Angeles
Los Angeles, United States
2 h-index
5 publications
304 citations
American University
Washington, United States
2 h-index
4 publications
58 citations
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10 publications · 1.3K citations
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Repository · 8 publications · 143 citations
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4 publications · 210 citations
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3 publications · 541 citations
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2 publications · 37 citations
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