Multiple Imputation Diagnostics

Multiple Imputation Diagnostics is a methodological framework used to assess the validity, convergence, and reliability of imputed datasets by evaluating the consistency and plausibility of multiple imputations relative to the observed data and underlying missingness assumptions.

38

Publications

3.4K

Citations

373

Authors

132

Institutions

Publications per year

2017–2026

2

Authors

373

  1. AG
    Andrew Gelman

    Columbia University, United States

    5 publications · 508 citations

    5

    h-index

  2. TE
    Trivellore E. Raghunathan

    University of Michigan, United States

    4 publications · 213 citations

    4

    h-index

  3. YS
    Yu‐Sung Su

    Tsinghua University, China

    2 publications · 769 citations

    2

    h-index

  4. KA
    Kobi Abayomi

    Columbia University, United States

    2 publications · 228 citations

    2

    h-index

  5. GK
    Gary King

    Harvard University Press, United States

    2 publications · 160 citations

    2

    h-index

1–5 of 373

Venues

  1. 4

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  2. Statistics in Medicine

    2 publications · 92 citations

    2

    h-index

  3. American Journal of Epidemiology

    AJE · 2 publications · 345 citations

    2

    h-index

  4. SSRN Electronic Journal

    Repository · 2 publications · 47 citations

    2

    h-index

  5. 1

    h-index

1–5