Bootstrap For Spatial Data
Bootstrap for Spatial Data is a resampling technique that extends the bootstrap method to spatial datasets by preserving spatial dependence structures through specialized sampling schemes that account for spatial autocorrelation and geographic proximity.
29
Publications
972
Citations
73
Authors
53
Institutions
Publications per year
2017–2026
9
Parents
29
1–5 of 29
73
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53
Lancaster University
Lancaster, United Kingdom
2 h-index
2 publications
171 citations

Universitat Jaume I
Castellon, Spain
2 h-index
2 publications
171 citations
University of Wisconsin–Madison
Madison, United States
2 h-index
3 publications
151 citations

Universidade de Vigo
Vigo, Spain
2 h-index
3 publications
79 citations

University of Liverpool
Liverpool, United Kingdom
2 h-index
2 publications
171 citations
Institutes of Science and Development
Beijing, China
1 h-index
1 publications
24 citations
Texas A&M University
College Station, United States
1 h-index
1 publications
21 citations
The Ohio State University
Columbus, United States
1 h-index
1 publications
36 citations
1–8 of 53
CSDA · 3 publications · 36 citations
3
h-index
2 publications · 69 citations
2
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AAP · 2 publications · 171 citations
2
h-index
1 publications · 32 citations
1
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1 publications · 37 citations
1
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1–5