Kernel Regression
Kernel Regression is a nonparametric regression method that estimates the conditional expectation of a dependent variable by weighting nearby observed data points according to a kernel function, thereby distinguishing itself from parametric regression techniques through its reliance on local smoothing rather than global functional form assumptions.
1.8K
Publications
148.4K
Citations
4.2K
Authors
1.5K
Institutions
Publications per year
2017–2026
568
1.8K
1–5 of 1.8K
4.2K
16
h-index
16
h-index
14
h-index
13
h-index
12
h-index
1–5 of 4.2K
1.5K
Texas A&M University
College Station, United States
24 h-index
45 publications
4K citations
University of Michigan
Ann Arbor, United States
22 h-index
50 publications
3.1K citations
Harvard University
Cambridge, United States
21 h-index
76 publications
13K citations
University of California, Berkeley
Berkeley, United States
19 h-index
46 publications
6.7K citations

Centre National de la Recherche Scientifique
Paris, France
18 h-index
54 publications
2K citations

Chinese Academy of Sciences
Beijing, China
18 h-index
35 publications
2K citations
Carnegie Mellon University
Pittsburgh, United States
17 h-index
36 publications
2.3K citations
Massachusetts Institute of Technology
Cambridge, United States
16 h-index
28 publications
2.8K citations
1–8 of 1.5K
69 publications · 9.7K citations
50
h-index
75 publications · 11.3K citations
48
h-index
47 publications · 1.9K citations
24
h-index
Repository · 54 publications · 2.1K citations
23
h-index
24 publications · 848 citations
18
h-index
1–5