Bootstrap For Functional Data
Bootstrap for Functional Data is a statistical resampling methodology that belongs to the class of nonparametric bootstrap techniques and is specifically distinguished by its adaptation to functional observations, where data are treated as realizations of random curves or functions rather than scalar or vector-valued random variables.
18
Publications
755
Citations
43
Authors
35
Institutions
Publications per year
2017–2026
6
Parents
18
1–5 of 18
43
2
h-index
2
h-index
1
h-index
1
h-index
1
h-index
1–5 of 43
35
National University of Singapore
Singapore, Singapore
2 h-index
2 publications
73 citations
Universidade de Santiago de Compostela
Santiago de Compostela, Spain
2 h-index
3 publications
282 citations
Columbia University
New York, United States
2 h-index
2 publications
134 citations
Ludwig-Maximilians-Universität München
Munich, Germany
1 h-index
1 publications
123 citations
Laboratoire de Mathématiques et Applications
Poitiers, France
1 h-index
1 publications
19 citations
University of Tennessee at Knoxville
Knoxville, United States
1 h-index
1 publications
21 citations
University of Illinois Urbana-Champaign
Urbana, United States
1 h-index
1 publications
21 citations

Australian National University
Canberra, Australia
1 h-index
1 publications
16 citations
1–8 of 35
4 publications · 62 citations
4
h-index
2 publications · 79 citations
2
h-index
AOAS · 1 publications · 15 citations
1
h-index
1 publications · 13 citations
1
h-index
CSDA · 1 publications · 184 citations
1
h-index
1–5