Subject-specific Effects

Subject-Specific Effects is a statistical modeling construct that represents individual-level random variations in the response probabilities over time, distinguishing itself from population-averaged effects by accounting for within-subject correlation through conditional inference given the subject’s latent random effects in longitudinal categorical data analysis.

558

Publications

38.4K

Citations

1.8K

Authors

744

Institutions

Publications per year

2017–2026

180

Authors

1.8K

  1. CR
    Craig R. Rush

    University of Kentucky, United States

    19 publications · 582 citations

    15

    h-index

  2. WW
    William W. Stoops

    University of Kentucky, United States

    12 publications · 380 citations

    12

    h-index

  3. JA
    Joshua A. Lile

    University of Kentucky, United States

    7 publications · 250 citations

    7

    h-index

  4. LR
    Lon R. Hays

    University of Kentucky, United States

    7 publications · 217 citations

    7

    h-index

  5. KF
    Karl Friston

    University College London, United Kingdom

    7 publications · 564 citations

    7

    h-index

1–5 of 1.8K

Venues

  1. Statistics in Medicine

    18 publications · 978 citations

    15

    h-index

  2. NeuroImage

    15 publications · 3.7K citations

    15

    h-index

  3. Biometrics

    13 publications · 829 citations

    12

    h-index

  4. Journal of the American Statistical Association

    11 publications · 317 citations

    11

    h-index

  5. Journal of Biomechanics

    8 publications · 378 citations

    8

    h-index

1–5