Nonparametric Mixed-effects Models
Nonparametric Mixed-Effects Models is a statistical modeling framework that extends mixed-effects models by relaxing parametric assumptions about the functional form of fixed and/or random effects, allowing flexible, data-driven estimation through nonparametric methods such as splines, kernels, or Gaussian processes.
110
Publications
18K
Citations
192
Authors
151
Institutions
Publications per year
2017–2026
9
Parents
110
Non-Parametric Statistics for the Behavioral Sciences.
Alan Stuart, Sidney Siegel · Economica · 1957 · 3.4K citations
Sidney Siegel · The American Statistician · 1957 · 2.8K citations
Sidney Siegel · The American Statistician · 1957 · 1.2K citations
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151
University of California, Santa Barbara
Santa Barbara, United States
5 h-index
6 publications
592 citations

Pennsylvania State University
State College, United States
4 h-index
5 publications
2.9K citations
The University of Texas MD Anderson Cancer Center
Houston, United States
3 h-index
6 publications
1.6K citations
Harvard University
Cambridge, United States
3 h-index
6 publications
218 citations

University of Toronto
Toronto, Canada
3 h-index
3 publications
1K citations

Université Joseph Fourier
Grenoble, France
3 h-index
3 publications
75 citations
Université Paris Cité
Paris, France
3 h-index
3 publications
41 citations

University of Rochester
Rochester, United States
3 h-index
6 publications
341 citations
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24 publications · 4K citations
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7 publications · 655 citations
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4 publications · 142 citations
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CSDA · 4 publications · 66 citations
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4 publications · 250 citations
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