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)
log
μ