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

Authors

192

  1. YW
    Yuedong Wang

    University of California, Santa Barbara, United States

    4 publications · 530 citations

    4

    h-index

  2. RT
    Robert Tibshirani

    Stanford University, United States

    3 publications · 1K citations

    3

    h-index

  3. HL
    Hua Liang

    George Washington University, United States

    3 publications · 113 citations

    3

    h-index

  4. SS
    Sidney Siegel

    Pennsylvania State University, United States

    3 publications · 7.4K citations

    3

    h-index

  5. CM
    Ciprian M. Crainiceanu

    Johns Hopkins University, United States

    3 publications · 181 citations

    3

    h-index

1–5 of 192

Venues

  1. 20

    h-index

  2. Biometrics

    7 publications · 655 citations

    7

    h-index

  3. Journal of Statistical Planning and Inference

    4 publications · 142 citations

    4

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 4 publications · 66 citations

    4

    h-index

  5. Journal of Multivariate Analysis

    4 publications · 250 citations

    4

    h-index

1–5