Work

Univariate-Guided Sparse Regression

Year: 2025

Type: article

Abstract: In this article, we introduce "uniLasso," a novel statistical method for 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 data sets, we demonstrate that uniLasso outperforms lasso in various settings, particularly in terms of sparsity and model interpretability. We prove asymptotic support recovery and mean-squared error consistency under a set of conditions different from the well-known irrepresentability conditions for the lasso. Extensions to generalized linear models (GLMs) and Cox regression are also discussed.

Source: Harvard Data Science Review

DOI: 10.1162/99608f92.c79ff6db

Authors: Sourav Chatterjee, Trevor Hastie, Robert Tibshirani

Institution: Stanford University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, National Institutes of Health

Awards: Matrix Completion with Non-uniform Missing Patterns, a New Measure of Conditional Dependence, and Applications to Feature Selection, New Statistical Methods for Medical Signals and Images, Mathematical Foundations for Yang-Mills Theory, Randomly Growing Surfaces, and Related Systems, Flexible and Adaptive Statistical Modeling, New Statistical Methods for Medical Signals and Images