Misclassification Models

Misclassification Models is a statistical modeling framework within Bayesian measurement error models that specifically accounts for systematic errors in categorical variable classification by incorporating probabilistic representations of misassignment to distinguish true underlying states from observed erroneous classifications.

832

Publications

46.7K

Citations

2.5K

Authors

1K

Institutions

Publications per year

2017–2026

154

Variants

Misclassification Error Models

Authors

2.5K

  1. PG
    Paul Gustafson

    University of British Columbia, Canada

    10 publications · 522 citations

    10

    h-index

  2. PA
    Peter A. Lachenbruch

    University of California, Los Angeles, United States

    10 publications · 2.4K citations

    10

    h-index

  3. TM
    Taghi M. Khoshgoftaar

    Florida Atlantic University, United States

    9 publications · 420 citations

    9

    h-index

  4. EB
    Edward B. Allen

    Florida Atlantic University, United States

    9 publications · 420 citations

    9

    h-index

  5. JD
    James D. Stamey

    Baylor University, United States

    8 publications · 214 citations

    8

    h-index

1–5 of 2.5K

Venues

  1. Biometrics

    29 publications · 2K citations

    21

    h-index

  2. Statistics in Medicine

    41 publications · 1.3K citations

    19

    h-index

  3. Epidemiology

    24 publications · 1.4K citations

    19

    h-index

  4. 18

    h-index

  5. American Journal of Epidemiology

    AJE · 20 publications · 2.7K citations

    18

    h-index

1–5