164 citations · 167 across the 6 of their papers we have counts for
6 papers
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…
MMIL: A novel algorithm for disease associated cell type discovery
Erin Craig, Timothy Keyes, Jolanda Sarno +5
Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. To address this, we introduce Mixture Modeling for Multiple…
Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank
Thomas Le Menestrel, Erin Craig, Robert Tibshirani +2
Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals, underscoring…
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)…