Stability Selection via Variable Decorrelation
The method decorrelates variables before applying Lasso to improve selection stability. By reordering and orthogonally transforming the design matrix, it ensures that Lasso consistently selects relevant variables across different data sets, even when variables are highly correlated. This leads to enhanced selection stability and predictive accuracy compared to standard methods.