Kernel Smoothing
Kernel Smoothing is a nonparametric density estimation method that belongs to the class of spatial smoothing techniques, distinguished by its use of a kernel function to assign weighted contributions to nearby observations in order to estimate the underlying probability density function at a given point in space.
692
Publications
83.5K
Citations
1.3K
Authors
658
Institutions
Publications per year
2017–2026
95
692
1–5 of 692
1.3K
11
h-index
University of North Carolina at Chapel Hill, United States
9 publications · 852 citations
9
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9
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8
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8
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1–5 of 1.3K
658
Texas A&M University
College Station, United States
14 h-index
22 publications
1.4K citations

The Open University
Milton Keynes, United Kingdom
13 h-index
18 publications
3.2K citations

Rice University
Houston, United States
12 h-index
17 publications
1.9K citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
12 h-index
12 publications
2.5K citations

Cornell University
Ithaca, United States
12 h-index
12 publications
1.1K citations
Columbia University
New York, United States
10 h-index
15 publications
1.7K citations

Centre National de la Recherche Scientifique
Paris, France
10 h-index
21 publications
621 citations

Johns Hopkins University
Baltimore, United States
9 h-index
13 publications
1.1K citations
1–8 of 658
64 publications · 14.6K citations
42
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44 publications · 8.4K citations
36
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29 publications · 923 citations
17
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15 publications · 3.2K citations
15
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16 publications · 2.1K citations
13
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