Expectation-maximization Imputation

Expectation-Maximization Imputation is a statistical imputation method that iteratively applies the expectation-maximization algorithm to estimate missing data by maximizing the likelihood of observed data under an assumed probability model.

188

Publications

15.7K

Citations

559

Authors

321

Institutions

Publications per year

2017–2026

78

Authors

559

  1. LD
    L. D. Dhinesh Babu

    Vellore Institute of Technology University, India

    2 publications · 39 citations

    2

    h-index

  2. LS
    Lijun Sun

    McGill University, Canada

    2 publications · 257 citations

    2

    h-index

  3. MZ
    Md Zahidul Islam

    Charles Sturt University, Australia

    2 publications · 111 citations

    2

    h-index

  4. RR
    Roozbeh Razavi‐Far

    University of Windsor, Canada

    2 publications · 154 citations

    2

    h-index

  5. LE
    Lynne E. Parker

    University of Tennessee at Knoxville, United States

    2 publications · 74 citations

    2

    h-index

1–5 of 559

Venues

  1. Journal of the American Statistical Association

    6 publications · 2.6K citations

    6

    h-index

  2. Neurocomputing

    4 publications · 318 citations

    5

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 5 publications · 136 citations

    5

    h-index

  4. 4

    h-index

  5. American Journal of Epidemiology

    AJE · 3 publications · 340 citations

    3

    h-index

1–5