164 citations · 167 across the 6 of their papers we have counts for
4 papers · 1 filter
Univariate-Guided Sparse Regression for Biobank-Scale High-Dimensional Omics Data
Joshua Richland, Tuomo Kiiskinen, William Wang +5
We present a scalable framework for computing polygenic risk scores (PRS) in high-dimensional genomic settings using the recently introduced Univariate-Guided Sparse Regression (un…
Pre-validation Revisited
Jing Shang, Sourav Chatterjee, Trevor Hastie +1
Pre-validation is a way to build prediction model with two datasets of significantly different feature dimensions. Previous work showed that the asymptotic distribution of the resu…
Univariate-Guided Sparse Regression
Sourav Chatterjee, Trevor Hastie, Robert Tibshirani
In this paper, we introduce ``UniLasso'' -- a novel statistical method for sparse regression. This two-stage approach preserves the signs of the univariate coefficients and leverag…
Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso
Trevor Hastie, Robert Tibshirani, Ryan J. Tibshirani
In exciting new work, Bertsimas et al. (2016) showed that the classical best subset selection problem in regression modeling can be formulated as a mixed integer optimization (MIO)…