Ignorable Missingness

Ignorable Missingness is a missingness mechanism in which the probability of data being missing depends only on observed variables, making the missing data process separable from the substantive model under the assumption of parameter distinctness and missing at random.

80

Publications

5.9K

Citations

174

Authors

129

Institutions

Publications per year

2017–2026

17

Authors

174

  1. DB
    Donald B. Rubin

    Harvard University, United States

    6 publications · 982 citations

    6

    h-index

  2. JM
    James M. Robins

    Harvard University, United States

    5 publications · 2.2K citations

    5

    h-index

  3. DF
    Daniel F. Heitjan

    University of Pennsylvania, United States

    5 publications · 489 citations

    5

    h-index

  4. SL
    Sik‐Yum Lee

    Chinese University of Hong Kong, Hong Kong SAR China

    4 publications · 143 citations

    4

    h-index

  5. NS
    Nathaniel Schenker

    National Center for Health Statistics, United States

    3 publications · 984 citations

    3

    h-index

1–5 of 174

Venues

  1. Biometrika

    10 publications · 337 citations

    10

    h-index

  2. Statistics in Medicine

    10 publications · 407 citations

    10

    h-index

  3. Biometrics

    8 publications · 2.2K citations

    8

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 3 publications · 87 citations

    3

    h-index

  5. Statistical Methods in Medical Research

    3 publications · 57 citations

    3

    h-index

1–5