Missing Data Model Selection

Missing Data Model Selection is a model selection framework that specifically addresses the challenge of choosing optimal statistical models for categorical data in the presence of missing values by incorporating mechanisms to account for missingness patterns and their impact on model performance.

151

Publications

18.2K

Citations

467

Authors

275

Institutions

Publications per year

2017–2026

52

Authors

467

  1. GM
    Geert Molenberghs

    Hasselt University, Belgium

    5 publications · 169 citations

    5

    h-index

  2. JJ
    Julie Josse

    École Polytechnique, France

    4 publications · 326 citations

    4

    h-index

  3. IR
    Ian R. White

    MRC Biostatistics Unit, United Kingdom

    3 publications · 9.7K citations

    3

    h-index

  4. JR
    James R. Carpenter

    London School of Hygiene & Tropical Medicine, United Kingdom

    3 publications · 79 citations

    3

    h-index

  5. JG
    Joseph G. Ibrahim

    University of North Carolina at Chapel Hill, United States

    3 publications · 250 citations

    3

    h-index

1–5 of 467

Venues

  1. Journal of the American Statistical Association

    11 publications · 1.7K citations

    11

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 9 publications · 306 citations

    9

    h-index

  3. Statistics in Medicine

    9 publications · 9.9K citations

    9

    h-index

  4. Biometrics

    6 publications · 243 citations

    6

    h-index

  5. Journal of Multivariate Analysis

    3 publications · 72 citations

    3

    h-index

1–5