Bootstrap For Small Samples
Bootstrap for Small Samples is a resampling methodology within the class of bootstrap techniques that specifically addresses the challenges of statistical inference in small sample sizes by generating multiple synthetic samples through random sampling with replacement to improve estimation accuracy and reliability when traditional asymptotic methods are inadequate.
215
Publications
18.4K
Citations
383
Authors
259
Institutions
Publications per year
2017–2026
26
Parents
215
Error Bands for Impulse Responses
Christopher A. Sims, Tao Zha · Econometrica · 1999 · 957 citations
On the statistical analysis of capture experiments
Richard Huggins · Biometrika · 1989 · 713 citations
On blocking rules for the bootstrap with dependent data
Peter Hall, Joël L. Horowitz, Bing‐Yi Jing · Biometrika · 1995 · 599 citations
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Queen's University
Kingston, Canada
7 h-index
7 publications
1.2K citations

Australian National University
Canberra, Australia
7 h-index
8 publications
1K citations

Carleton University
Ottawa, Canada
5 h-index
5 publications
822 citations
University of Southern California
Los Angeles, United States
4 h-index
5 publications
193 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
4 h-index
5 publications
755 citations

Queensland University of Technology
Brisbane, Australia
4 h-index
4 publications
672 citations
Federal Reserve
Washington D.C., United States
3 h-index
3 publications
512 citations

UNSW Sydney
Sydney, Australia
3 h-index
3 publications
127 citations
1–8 of 259
12 publications · 281 citations
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10 publications · 671 citations
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7 publications · 571 citations
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CJS · 5 publications · 190 citations
5
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5 publications · 181 citations
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