Mixed Model Diagnostics

Mixed Model Diagnostics is a statistical methodology used to assess the validity, assumptions, and performance of mixed effects models by identifying influential observations, detecting model misspecification, and evaluating the fit of both fixed and random effects structures.

35

Publications

2.8K

Citations

76

Authors

54

Institutions

Publications per year

2017–2026

5

Authors

76

  1. TZ
    Temesgen Zewotir

    University of KwaZulu-Natal, South Africa

    3 publications · 78 citations

    3

    h-index

  2. GM
    Geert Molenberghs

    Hasselt University, Belgium

    3 publications · 74 citations

    3

    h-index

  3. FM
    Francisco Marcelo Monteiro da Rocha

    Universidade Federal de São Paulo, Brazil

    2 publications · 56 citations

    2

    h-index

  4. RD
    R. Dennis Cook

    University of Minnesota, United States

    2 publications · 206 citations

    2

    h-index

  5. JW
    J. W. Green

    Texas A&M University, United States

    2 publications · 38 citations

    2

    h-index

1–5 of 76

Venues

  1. Technometrics

    6 publications · 467 citations

    6

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 2 publications · 79 citations

    2

    h-index

  3. Biometrika

    2 publications · 90 citations

    2

    h-index

  4. 2

    h-index

  5. Journal of Computational and Graphical Statistics

    JCGS · 1 publications · 32 citations

    1

    h-index

1–5