Binary Response Models

Binary Response Models is a class of statistical regression models in categorical data analysis that describe the relationship between a set of explanatory variables and a single binary outcome by modeling the conditional probability of one of the two response categories, typically via an appropriate link function (such as logit or probit) in a generalized linear modeling framework.

901

Publications

101K

Citations

1.5K

Authors

715

Institutions

Publications per year

2017–2026

107

Variants

Binary Choice ModelsBinary Response AnalysisMultinomial Response ModelsNominal Response AnalysisOrdinal Response AnalysisOrdinal Response Models

Authors

1.5K

  1. GM
    Garrett M. Fitzmaurice

    Harvard University, United States

    18 publications · 1.7K citations

    16

    h-index

  2. SR
    Stuart R. Lipsitz

    Brigham and Women's Hospital, United States

    17 publications · 1.5K citations

    16

    h-index

  3. NM
    Nan M. Laird

    Harvard University, United States

    12 publications · 2K citations

    12

    h-index

  4. AA
    Alan Agresti

    University of Florida, United States

    13 publications · 1.1K citations

    12

    h-index

  5. GM
    Geert Molenberghs

    Hasselt University, Belgium

    10 publications · 380 citations

    10

    h-index

1–5 of 1.5K

Venues

  1. Journal of the American Statistical Association

    58 publications · 10K citations

    40

    h-index

  2. Statistics in Medicine

    83 publications · 4.7K citations

    38

    h-index

  3. Biometrics

    66 publications · 9K citations

    38

    h-index

  4. Biometrika

    43 publications · 5K citations

    30

    h-index

  5. Journal of Econometrics

    26 publications · 3.7K citations

    19

    h-index

1–5