Bootstrap for Missing Data is a statistical methodology within the class of resampling techniques that specifically adapts the bootstrap framework to account for uncertainty and variability in data sets with missing values by resampling from imputed or partially observed data to estimate the sampling distribution of a statistic.
24
Publications
1.5K
Citations
42
Authors
35
Institutions
Publications per year
2017–2026
4
Parents
Publications
24
Bootstrap for Imputed Survey Data
Jun Shao, R. R. Sitter · Journal of the American Statistical Association · 1996 · 182 citations
1–5 of 24
Authors
42
- RR
2
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- BE
2
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- SC
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- CL
1
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- VJ
1
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1–5 of 42
Institutions
35
United States Geological Survey
Reston, United States
2 h-index
2 publications
28 citations
Stanford University
Stanford, United States
2 h-index
2 publications
514 citations
Simon Fraser University
Burnaby, Canada
2 h-index
2 publications
225 citations
University of Wisconsin–Madison
Madison, United States
2 h-index
2 publications
225 citations
University of Groningen
Groningen, The Netherlands
1 h-index
1 publications
16 citations
University of Maryland, College Park
College Park, United States
1 h-index
1 publications
94 citations
Leiden University
Leiden, The Netherlands
1 h-index
1 publications
16 citations
University at Buffalo, State University of New York
Buffalo, United States
1 h-index
1 publications
86 citations
1–8 of 35
Venues
- Journal of the American Statistical Association
5 publications · 758 citations
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- Statistical Science
1 publications · 94 citations
1
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- Biometrika
1 publications · 22 citations
1
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- Journal of the Royal Statistical Society Series A (Statistics in Society)
1 publications · 14 citations
1
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- Stochastic Environmental Research and Risk Assessment
1 publications · 34 citations
1
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1–5