Cross-validated Model Averaging
Cross-Validated Model Averaging is a model averaging technique that assigns weights to candidate models based on their predictive performance evaluated through cross-validation, thereby ensuring robustness against overfitting by leveraging out-of-sample accuracy in the weighting scheme.
5
Publications
73
Citations
13
Authors
12
Institutions
Publications per year
2017–2026
4
Parents
5
On cross-validation and stacking
Claudia Perlich, Grzegorz Świrszcz · ACM SIGKDD Explorations Newsletter · 2011 · 22 citations
Cross-Validation Model Averaging for Generalized Functional Linear Model
Haili Zhang, Guohua Zou · Econometrics · 2020 · 12 citations · Full text
Business, Economics, Econometrics +15
1–5 of 5
13
Bernstein Center for Computational Neuroscience Berlin, Germany
1 publications · 12 citations
1
h-index
1
h-index
Bernstein Center for Computational Neuroscience Berlin, Germany
1 publications · 12 citations
1
h-index
1
h-index
Institute of Statistical Science, Academia Sinica, Taiwan
1 publications · 16 citations
1
h-index
1–5 of 13
12
Academy of Mathematics and Systems Science
Beijing, China
3 h-index
3 publications
39 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
3 publications
39 citations
University of Science and Technology of China
Hefei, China
1 h-index
1 publications
11 citations

Capital Normal University
Beijing, China
1 h-index
1 publications
12 citations
NYC Media
Manhattan, United States
1 h-index
1 publications
22 citations
University of Chinese Academy of Sciences
Beijing, China
1 h-index
1 publications
12 citations

Pennsylvania State University
State College, United States
1 h-index
1 publications
16 citations
Charité - Universitätsmedizin Berlin
Berlin, Germany
1 h-index
1 publications
12 citations
1–8 of 12
1 publications · 22 citations
1
h-index
1 publications · 11 citations
1
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1 publications · 16 citations
1
h-index
1 publications · 12 citations
1
h-index
1 publications · 12 citations
1
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