Bayesian Missing Data Analysis

Bayesian Missing Data Analysis is a statistical methodology that belongs to the class of Bayesian inference techniques and is distinguished by its formal integration of missing data mechanisms into the probabilistic model through the specification of prior distributions and joint modeling of observed and missing values.

220

Publications

28K

Citations

544

Authors

279

Institutions

Publications per year

2017–2026

45

Authors

544

  1. JG
    Joseph G. Ibrahim

    University of North Carolina at Chapel Hill, United States

    8 publications · 240 citations

    8

    h-index

  2. JP
    Jerome P. Reiter

    Duke University, United States

    6 publications · 258 citations

    6

    h-index

  3. SL
    Sik‐Yum Lee

    Chinese University of Hong Kong, Hong Kong SAR China

    5 publications · 183 citations

    5

    h-index

  4. TE
    Trivellore E. Raghunathan

    University of Michigan, United States

    5 publications · 116 citations

    5

    h-index

  5. XS
    Xinyuan Song

    Chinese University of Hong Kong, Hong Kong SAR China

    5 publications · 163 citations

    5

    h-index

1–5 of 544

Venues

  1. Journal of the American Statistical Association

    17 publications · 2.8K citations

    13

    h-index

  2. Statistics in Medicine

    16 publications · 537 citations

    12

    h-index

  3. Biometrics

    7 publications · 431 citations

    7

    h-index

  4. Biometrika

    7 publications · 11.6K citations

    7

    h-index

  5. 5

    h-index

1–5