Figure 2.

Penalization functions used for the iterative estimation of sparse causal relations. At each step of the iterative process, the regression coefficients of each node with all others are weighted according to their current size. Many coefficients are successively down-weighted and ultimately set to zero—effectively carrying out variable selection. y-Axis: weight according to current value of a regression coefficient β (x-axis). Each curve corresponds to a different type of penalization: heavy line, L2 norm (ridge regression); dashed, L1 norm (LASSO). Dotted, Hard-Threshold; dash-dot, SCAD; light line, mixture.