Weighted Estimating Equations

Weighted Estimating Equations is a statistical method for parameter estimation in the presence of missing data, in which observation-specific weights are incorporated into estimating equations to account for unequal probabilities of missingness, thereby yielding consistent and asymptotically normal estimators under misspecified or correct models.

89

Publications

10.2K

Citations

167

Authors

104

Institutions

Publications per year

2017–2026

8

Variants

Wee

Authors

167

  1. SR
    Stuart R. Lipsitz

    Brigham and Women's Hospital, United States

    7 publications · 832 citations

    7

    h-index

  2. LP
    Lue Ping Zhao

    Fred Hutch Cancer Center, United States

    5 publications · 3.4K citations

    5

    h-index

  3. JG
    Joseph G. Ibrahim

    University of North Carolina at Chapel Hill, United States

    4 publications · 629 citations

    4

    h-index

  4. BG
    Bruce G. Lindsay

    Pennsylvania State University, United States

    4 publications · 243 citations

    4

    h-index

  5. AB
    Ayanendranath Basu

    Indian Statistical Institute, India

    3 publications · 164 citations

    3

    h-index

1–5 of 167

Venues

  1. Biometrics

    12 publications · 352 citations

    11

    h-index

  2. Journal of the American Statistical Association

    10 publications · 3.9K citations

    10

    h-index

  3. Statistics in Medicine

    7 publications · 452 citations

    7

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 4 publications · 158 citations

    4

    h-index

  5. Biometrika

    4 publications · 178 citations

    4

    h-index

1–5