Surrogate Variable Models

Surrogate Variable Models is a statistical modeling framework within Bayesian measurement error models that uses auxiliary variables as proxies for unobserved true predictors, distinguishing itself by enabling valid inference in the presence of measurement error through explicit modeling of the relationship between observed surrogates and latent variables.

67

Publications

9.7K

Citations

253

Authors

131

Institutions

Publications per year

2017–2026

20

Variants

Surrogate Variable Model

Authors

253

  1. JT
    Jeffrey T. Leek

    Johns Hopkins University, United States

    7 publications · 3.2K citations

    7

    h-index

  2. JB
    John B. McKinlay

    New England Research Institutes, United States

    2 publications · 144 citations

    2

    h-index

  3. MR
    Michael R. Elliott

    University of Michigan, United States

    2 publications · 110 citations

    2

    h-index

  4. JD
    John D. Storey

    Princeton University, United States

    2 publications · 2.3K citations

    2

    h-index

  5. HA
    Henry A. Feldman

    Boston Children's Hospital, United States

    2 publications · 144 citations

    2

    h-index

1–5 of 253

Venues

  1. Statistics in Medicine

    3 publications · 2.1K citations

    3

    h-index

  2. Bioinformatics

    3 publications · 544 citations

    3

    h-index

  3. PLoS ONE

    3 publications · 143 citations

    3

    h-index

  4. Biostatistics

    2 publications · 545 citations

    2

    h-index

  5. Journal of Clinical Epidemiology

    2 publications · 144 citations

    2

    h-index

1–5