Regression Shrinkage and Selection via The Lasso: A Retrospective

Key Points

• To provide a retrospective overview of the original lasso formulation and review major theoretical and computational developments that emerged following its introduction.
• Reviewed the core principles and historical context of least absolute shrinkage and selection operator (lasso) regression.
• Surveyed key methodological advancements, algorithmic implementations, and high-dimensional extensions developed since the original proposal.
• Outlines how applying an L1 penalty achieves simultaneous continuous coefficient shrinkage and automatic variable selection.
• Synthesizes key theoretical guarantees, computational algorithms, and widespread extensions across modern high-dimensional statistical modeling.