Likelihood For Missing Data

Likelihood for Missing Data is a statistical framework that specifies the probability of observed data under a given model while accounting for the uncertainty introduced by unobserved or missing values through formal probabilistic integration over the missing data mechanism.

166

Publications

69.3K

Citations

320

Authors

235

Institutions

Publications per year

2017–2026

25

Authors

320

  1. LX
    Liugen Xue

    Beijing University of Technology, China

    6 publications · 173 citations

    6

    h-index

  2. DB
    Donald B. Rubin

    Harvard University, United States

    5 publications · 50.1K citations

    5

    h-index

  3. RR
    R. R. Hocking

    Mississippi State University, United States

    5 publications · 519 citations

    5

    h-index

  4. JG
    Joseph G. Ibrahim

    University of North Carolina at Chapel Hill, United States

    4 publications · 111 citations

    4

    h-index

  5. YQ
    Yongsong Qin

    Guangxi Normal University, China

    4 publications · 71 citations

    4

    h-index

1–5 of 320

Venues

  1. 15

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 9 publications · 215 citations

    9

    h-index

  3. 8

    h-index

  4. Journal of Statistical Planning and Inference

    6 publications · 98 citations

    6

    h-index

  5. Statistics in Medicine

    6 publications · 164 citations

    6

    h-index

1–5