Missing-not-at-random Modeling

Missing-Not-At-Random Modeling is a statistical framework within implicit feedback modeling that specifically accounts for systematic patterns in missing data by assuming that the probability of data being missing depends on unobserved entries, thereby distinguishing it from missing-at-random and missing-completely-at-random mechanisms.

145

Publications

11.3K

Citations

353

Authors

199

Institutions

Publications per year

2017–2026

30

Authors

353

  1. GM
    Geert Molenberghs

    Hasselt University, Belgium

    13 publications · 1.7K citations

    12

    h-index

  2. MG
    Michael G. Kenward

    London School of Hygiene & Tropical Medicine, United Kingdom

    9 publications · 654 citations

    9

    h-index

  3. CM
    Craig Mallinckrodt

    Eli Lilly (United States), United States

    5 publications · 547 citations

    5

    h-index

  4. GV
    Geert Verbeke

    KU Leuven, Belgium

    5 publications · 289 citations

    5

    h-index

  5. IR
    Ian R. White

    MRC Biostatistics Unit, United Kingdom

    4 publications · 543 citations

    4

    h-index

1–5 of 353

Venues

  1. Biometrics

    11 publications · 385 citations

    11

    h-index

  2. Statistics in Medicine

    9 publications · 719 citations

    9

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 7 publications · 178 citations

    7

    h-index

  4. Structural Equation Modeling A Multidisciplinary Journal

    SEM · 6 publications · 319 citations

    6

    h-index

  5. arXiv (Cornell University)

    Repository · 6 publications · 119 citations

    6

    h-index

1–5