Optimal Kernel Choice
Optimal Kernel Choice is a method in nonparametric statistics that identifies the kernel function minimizing the asymptotic mean integrated squared error in estimating a probability density function via kernel density estimation.
206
Publications
13.6K
Citations
560
Authors
292
Institutions
Publications per year
2017–2026
67
Parents
206
A Method for Optimal Image Subtraction
C. Alard, Robert H. Lupton · The Astrophysical Journal · 1998 · 1.4K citations · Full text
Natural Sciences, Astronomy, Physics +14
Optimal kernel choice for large-scale two-sample tests
Arthur Gretton, Dino Sejdinović, Heiko Strathmann et al. · 2012 · 505 citations
1–5 of 206
560
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University of Illinois Urbana-Champaign, United States
4 publications · 453 citations
4
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4
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3
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3
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1–5 of 560
292

Australian National University
Canberra, Australia
6 h-index
8 publications
462 citations
Heidelberg University
Heidelberg, Germany
4 h-index
5 publications
467 citations
University of Illinois Urbana-Champaign
Urbana, United States
4 h-index
6 publications
530 citations

University of Electronic Science and Technology of China
Chengdu, China
4 h-index
6 publications
506 citations
University of California San Diego
San Diego, United States
4 h-index
5 publications
876 citations

National University of Defense Technology
Changsha, China
3 h-index
10 publications
250 citations

Shanghai Jiao Tong University
Shanghai, China
3 h-index
10 publications
258 citations
UCLouvain
Louvain-la-Neuve, Belgium
3 h-index
5 publications
539 citations
1–8 of 292
8 publications · 1.1K citations
8
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7 publications · 194 citations
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5 publications · 606 citations
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Repository · 5 publications · 78 citations
5
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4 publications · 290 citations
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