Structural Zero Models

Structural Zero Models is a statistical modeling framework within categorical data analysis that accounts for structural zeros by explicitly distinguishing between categories that are impossible by definition and those that are merely unobserved due to sampling variation.

22

Publications

851

Citations

48

Authors

43

Institutions

Publications per year

2017–2026

5

Authors

48

  1. WT
    Wan Tang

    University of Rochester, United States

    3 publications · 91 citations

    3

    h-index

  2. DM
    Daniel Manrique‐Vallier

    Duke University, United States

    3 publications · 99 citations

    3

    h-index

  3. HH
    Hua He

    Tulane University, United States

    3 publications · 91 citations

    3

    h-index

  4. JP
    Jerome P. Reiter

    Duke University, United States

    2 publications · 73 citations

    2

    h-index

  5. PC
    Paul Crits‐Christoph

    University of Pennsylvania, United States

    2 publications · 77 citations

    2

    h-index

1–5 of 48

Venues

  1. Biometrical Journal

    3 publications · 39 citations

    3

    h-index

  2. Statistics in Medicine

    2 publications · 47 citations

    2

    h-index

  3. Biometrics

    2 publications · 121 citations

    2

    h-index

  4. Appetite

    1 publications · 24 citations

    1

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 1 publications · 10 citations

    1

    h-index

1–5