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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