Loess Smoothing
Loess Smoothing is a nonparametric regression method that fits localized polynomial models to data points using weighted least squares, where the weights are determined by a kernel function based on the proximity of neighboring observations.
23
Publications
8K
Citations
64
Authors
38
Institutions
Publications per year
2017–2026
4
Variants
Locally Estimated Scatterplot SmoothingLocally Weighted Scatterplot SmoothingLoess
23
1–5 of 23
64
2
h-index
2
h-index
1
h-index
1
h-index
TH Köln - University of Applied Sciences, Germany
1 publications · 86 citations
1
h-index
1–5 of 64
38
Core Competence
Santa Fe, United States
2 h-index
2 publications
6.3K citations
AT&T (United States)
Bedminster, United States
2 h-index
2 publications
6.3K citations
Biomathematics and Statistics Scotland
Edinburgh, United Kingdom
2 h-index
4 publications
54 citations

University of Toronto
Toronto, Canada
2 h-index
2 publications
441 citations
Newmarket Equine Hospital
Newmarket, United Kingdom
1 h-index
3 publications
63 citations
University of Hertfordshire
Hatfield, United Kingdom
1 h-index
3 publications
156 citations
National University of Ireland, Maynooth
Maynooth, Ireland
1 h-index
2 publications
30 citations

Sunnybrook Health Science Centre
Toronto, Canada
1 h-index
1 publications
357 citations
1–8 of 38
2 publications · 6.3K citations
2
h-index
1 publications · 23 citations
1
h-index
GJI · 1 publications · 29 citations
1
h-index
1 publications · 516 citations
1
h-index
1 publications · 357 citations
1
h-index
1–5