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Table 1 Link functions for linear, logistic and Poisson regression models in GLM, where different models have different A(), B(), and C()

From: Sparse generalized linear model with L 0 approximation for feature selection and prediction with big omics data

GLM models

B(θ)

μ(θ)=B (θ)

Link θ(μ)

V(μ)=B (θ)

Linear regression

θ 2/2

θ

Identity

1

Logistic regression

log(1+e θ)

\(\frac {1}{1 +e^{-{\theta }}}\)

logit

μ(1−μ)

Poisson regression

exp(θ)

exp(θ)

log

μ