Abstract: In this talk, we introduce "UniLasso" -- a novel statistical method for sparse regression. This two-stage approach preserves the signs of the univariate coefficients and leverages their magnitude. Both of these properties are attractive for stability and interpretation of the model. Through comprehensive simulations and applications to real-world datasets, we demonstrate that UniLasso outperforms Lasso in various settings, particularly in terms of sparsity and model interpretability. Extensions to generalized linear models (GLMs) and Cox regression are also discussed.

This is joint work with Sourav Chatterjee and Rob Tibshirani.