Generalized Linear Models

Generalized Linear Models is a statistical modeling framework that extends the classical linear model by allowing the response variable to follow a distribution from the exponential family and relating the mean to the predictors through a link function.

7.5K

Publications

1.2M

Citations

9.8K

Authors

2.7K

Institutions

Publications per year

2017–2026

1.1K

Authors

9.8K

  1. RJ
    Raymond J. Carroll

    Texas A&M University, United States

    50 publications · 5.9K citations

    33

    h-index

  2. JG
    Joseph G. Ibrahim

    University of North Carolina at Chapel Hill, United States

    31 publications · 3K citations

    28

    h-index

  3. GM
    Geert Molenberghs

    Hasselt University, Belgium

    35 publications · 1.3K citations

    22

    h-index

  4. SN
    Simon N. Wood

    University of Bath, United Kingdom

    21 publications · 18.8K citations

    20

    h-index

  5. RT
    Robert Tibshirani

    Stanford University, United States

    21 publications · 86.9K citations

    20

    h-index

1–5 of 9.8K

Venues

  1. Journal of the American Statistical Association

    471 publications · 119K citations

    129

    h-index

  2. Biometrika

    295 publications · 48.4K citations

    96

    h-index

  3. Biometrics

    210 publications · 48.8K citations

    85

    h-index

  4. Technometrics

    165 publications · 68K citations

    81

    h-index

  5. 73

    h-index

1–5