Parametric Bootstrap Testing
Parametric Bootstrap Testing is a statistical inference method that generates synthetic datasets under a specified parametric model to evaluate the goodness-of-fit of categorical models by comparing observed test statistics to their empirical distribution under the null hypothesis.
368
Publications
23.4K
Citations
789
Authors
454
Institutions
Publications per year
2017–2026
62
368
1–5 of 368
789
Universidade de Santiago de Compostela, Spain
6 publications · 479 citations
6
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5
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4
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4
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4
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1–5 of 789
454
University of Louisiana at Lafayette
Lafayette, United States
8 h-index
13 publications
629 citations
Stanford University
Stanford, United States
7 h-index
10 publications
463 citations
University of California San Diego
San Diego, United States
7 h-index
12 publications
1.3K citations
Texas A&M University
College Station, United States
6 h-index
7 publications
599 citations
University of Wisconsin–Madison
Madison, United States
6 h-index
13 publications
293 citations
Universidade de Santiago de Compostela
Santiago de Compostela, Spain
6 h-index
6 publications
441 citations

Universidade da Coruña
A Coruña, Spain
5 h-index
6 publications
447 citations
University of Southern California
Los Angeles, United States
5 h-index
5 publications
316 citations
1–8 of 454
17 publications · 364 citations
14
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14 publications · 1K citations
14
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11 publications · 605 citations
11
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12 publications · 263 citations
11
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Repository · 10 publications · 287 citations
10
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