Missing At Random Assumption

The Missing At Random Assumption is a statistical assumption that classifies the missingness mechanism by stipulating that, conditional on the observed data, the probability of missing values depends only on fully observed variables and not on the unobserved data.

137

Publications

9.3K

Citations

325

Authors

191

Institutions

Publications per year

2017–2026

33

Variants

Mar Assumption

Authors

325

  1. IR
    Ian R. White

    MRC Biostatistics Unit, United Kingdom

    9 publications · 2.3K citations

    9

    h-index

  2. JR
    James R. Carpenter

    London School of Hygiene & Tropical Medicine, United Kingdom

    6 publications · 289 citations

    6

    h-index

  3. MG
    Michael G. Kenward

    London School of Hygiene & Tropical Medicine, United Kingdom

    4 publications · 263 citations

    4

    h-index

  4. GM
    Geert Molenberghs

    Hasselt University, Belgium

    4 publications · 252 citations

    4

    h-index

  5. DA
    David Arterburn

    Kaiser Permanente Washington Health Research Institute, United States

    3 publications · 179 citations

    3

    h-index

1–5 of 325

Venues

  1. Statistics in Medicine

    13 publications · 1.4K citations

    12

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  2. Biometrics

    9 publications · 676 citations

    9

    h-index

  3. 9

    h-index

  4. Statistical Methods in Medical Research

    8 publications · 551 citations

    8

    h-index

  5. Biometrika

    8 publications · 341 citations

    8

    h-index

1–5