Multilevel Variable Selection

Multilevel Variable Selection is a statistical methodology within Bayesian multilevel modeling that systematically identifies and retains relevant predictors at multiple hierarchical levels by jointly estimating their inclusion probabilities while accounting for nested data structures and random effects.

71

Publications

6.3K

Citations

216

Authors

121

Institutions

Publications per year

2017–2026

22

Authors

216

  1. KT
    Kjell Tjessem

    University of Bergen, Norway

    2 publications · 367 citations

    2

    h-index

  2. JL
    Julio L. Peixoto

    University of Houston, United States

    2 publications · 156 citations

    2

    h-index

  3. KA
    Kelly A. Dyer

    University of Georgia, United States

    2 publications · 48 citations

    2

    h-index

  4. JJ
    John Jaenike

    University of Rochester, United States

    2 publications · 48 citations

    2

    h-index

  5. NK
    Nouna Kettaneh

    Umeå University, Sweden

    2 publications · 367 citations

    2

    h-index

1–5 of 216

Venues

  1. Chemometrics and Intelligent Laboratory Systems

    4 publications · 2.2K citations

    4

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 4 publications · 166 citations

    4

    h-index

  3. Evolution

    3 publications · 68 citations

    3

    h-index

  4. SSM - Population Health

    2 publications · 108 citations

    2

    h-index

  5. Biometrics

    2 publications · 433 citations

    2

    h-index

1–5