Link Functions

Link Functions is a mathematical transformation that specifies the functional relationship between the linear predictor and the expected value of the response variable in a generalized linear model, distinguishing it from other components by defining how the mean of the distribution depends on the explanatory variables.

309

Publications

33.7K

Citations

630

Authors

352

Institutions

Publications per year

2017–2026

67

Authors

630

  1. DK
    Dipak K. Dey

    University of Connecticut, United States

    9 publications · 377 citations

    9

    h-index

  2. YX
    Yingcun Xia

    National University of Singapore, Singapore

    5 publications · 221 citations

    5

    h-index

  3. CC
    Claudia Czado

    Technical University of Munich, Germany

    5 publications · 229 citations

    5

    h-index

  4. QS
    Qi-Man Shao

    University of Oregon, United States

    4 publications · 240 citations

    4

    h-index

  5. PC
    Paul C. Lambert

    University of Leicester, United Kingdom

    4 publications · 163 citations

    4

    h-index

1–5 of 630

Venues

  1. 21

    h-index

  2. The Annals of Statistics

    13 publications · 2.5K citations

    13

    h-index

  3. Biometrics

    12 publications · 647 citations

    12

    h-index

  4. Biometrika

    12 publications · 2.1K citations

    11

    h-index

  5. Statistics in Medicine

    9 publications · 1.1K citations

    9

    h-index

1–5