Missing Data Selection Models

Missing Data Selection Models is a statistical framework that belongs to the class of selection models used in the analysis of missing data, distinguished by its explicit modeling of the process through which data are missing as a function of observed and unobserved variables to correct for potential selection bias.

57

Publications

3K

Citations

142

Authors

104

Institutions

Publications per year

2017–2026

8

Variants

Selection Models For Missing Data

Authors

142

  1. GM
    Geert Molenberghs

    Hasselt University, Belgium

    5 publications · 302 citations

    5

    h-index

  2. MG
    Michael G. Kenward

    London School of Hygiene & Tropical Medicine, United Kingdom

    4 publications · 377 citations

    4

    h-index

  3. BM
    Bart Michiels

    KU Leuven, Belgium

    3 publications · 121 citations

    3

    h-index

  4. HT
    Herbert Thijs

    Hasselt University, Belgium

    3 publications · 268 citations

    3

    h-index

  5. SW
    Suojin Wang

    Texas A&M University, United States

    2 publications · 146 citations

    2

    h-index

1–5 of 142

Venues

  1. Biometrics

    7 publications · 225 citations

    7

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  2. Statistics in Medicine

    6 publications · 354 citations

    6

    h-index

  3. 3

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  4. Biometrical Journal

    2 publications · 31 citations

    2

    h-index

  5. Statistica Neerlandica

    2 publications · 170 citations

    2

    h-index

1–5