Figure 11
From: Controlling false discoveries in high-dimensional situations: boosting with stability selection
Number of false positives – Gaussian additive regression model. Boxplots for the number of false positives (FP) for all simulation settings with separate boxplots for the correlation settings (independent predictor variables or Toeplitz design), P F E R max and the assumption used to compute the error bound. Each observation in the boxplot is the average of the 50 simulation replicates. The open red circles represent the average number of false positives. The gray horizontal lines represent the error bounds.