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

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

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