Structural Measurement Error Models

Structural Measurement Error Models is a class of Bayesian measurement error models that explicitly incorporate assumptions about the underlying distribution of the true, unobserved covariates, distinguishing them from functional measurement error models by treating latent variables as random effects with a specified parametric or nonparametric prior structure.

61

Publications

4.1K

Citations

153

Authors

110

Institutions

Publications per year

2017–2026

10

Authors

153

  1. LA
    Leonard A. Stefanski

    North Carolina State University, United States

    5 publications · 395 citations

    5

    h-index

  2. RJ
    Raymond J. Carroll

    Texas A&M University, United States

    3 publications · 345 citations

    3

    h-index

  3. HS
    Hans Schneeweiß

    Ludwig-Maximilians-Universität München, Germany

    3 publications · 44 citations

    3

    h-index

  4. XH
    Xianzheng Huang

    University of South Carolina, United States

    3 publications · 65 citations

    3

    h-index

  5. YM
    Yeow Meng Thum

    University of California, Los Angeles, United States

    2 publications · 96 citations

    2

    h-index

1–5 of 153

Venues

  1. 5

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  2. Statistics in Medicine

    5 publications · 201 citations

    5

    h-index

  3. Biometrika

    3 publications · 322 citations

    3

    h-index

  4. 3

    h-index

  5. Biometrics

    3 publications · 187 citations

    3

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1–5