Missing Data Inference

Missing Data Inference is a statistical methodology within the broader class of inferential techniques that specifically addresses the problem of drawing valid conclusions from incomplete datasets by formally modeling the mechanisms underlying data absence and incorporating uncertainty due to missingness into the inferential process.

1.2K

Publications

129K

Citations

2.3K

Authors

978

Institutions

Publications per year

2017–2026

273

Authors

2.3K

  1. DB
    Donald B. Rubin

    Harvard University, United States

    27 publications · 14.6K citations

    23

    h-index

  2. RJ
    Roderick J. A. Little

    University of Michigan, United States

    26 publications · 6.7K citations

    22

    h-index

  3. IR
    Ian R. White

    MRC Biostatistics Unit, United Kingdom

    25 publications · 5K citations

    22

    h-index

  4. JR
    James R. Carpenter

    London School of Hygiene & Tropical Medicine, United Kingdom

    24 publications · 4.1K citations

    21

    h-index

  5. MG
    Michael G. Kenward

    London School of Hygiene & Tropical Medicine, United Kingdom

    20 publications · 1.9K citations

    18

    h-index

1–5 of 2.3K

Venues

  1. Journal of the American Statistical Association

    92 publications · 12.2K citations

    46

    h-index

  2. Statistics in Medicine

    89 publications · 5.2K citations

    37

    h-index

  3. Biometrics

    53 publications · 3.9K citations

    30

    h-index

  4. Biometrika

    35 publications · 13.8K citations

    23

    h-index

  5. arXiv (Cornell University)

    Repository · 28 publications · 1.3K citations

    18

    h-index

1–5