Missing Data Patterns

Missing Data Patterns is a methodological construct that identifies and classifies the systematic arrangements or configurations in which data values are absent across variables and observations in a dataset, distinguishing it from general data incompleteness by focusing on the structural regularity of omissions.

407

Publications

61.2K

Citations

1.4K

Authors

620

Institutions

Publications per year

2017–2026

109

Authors

1.4K

  1. RJ
    Roderick J. A. Little

    University of Michigan, United States

    15 publications · 26.7K citations

    15

    h-index

  2. GM
    Geert Molenberghs

    Hasselt University, Belgium

    7 publications · 200 citations

    7

    h-index

  3. DB
    Donald B. Rubin

    Harvard University, United States

    5 publications · 23.1K citations

    5

    h-index

  4. JR
    James R. Carpenter

    London School of Hygiene & Tropical Medicine, United Kingdom

    4 publications · 350 citations

    4

    h-index

  5. MG
    Michael G. Kenward

    London School of Hygiene & Tropical Medicine, United Kingdom

    3 publications · 124 citations

    3

    h-index

1–5 of 1.4K

Venues

  1. Journal of the American Statistical Association

    18 publications · 21.8K citations

    16

    h-index

  2. PubMed

    Repository · 9 publications · 236 citations

    9

    h-index

  3. Expert Systems with Applications

    7 publications · 351 citations

    7

    h-index

  4. Biometrics

    8 publications · 724 citations

    7

    h-index

  5. Statistics in Medicine

    7 publications · 252 citations

    7

    h-index

1–5