164 citations · 165 across the 7 of their papers we have counts for
8 papers
ERICA: Quantifying Replicability of Cluster Analysis
Siamak K. Sorooshyari, Manuel A. Rivas, Robert Tibshirani
Despite being ubiquitous in science, clustering lacks a unified framework for quantitatively evaluating the replicability of its results. We present evaluating replicability via it…
Nonparametric Regression Discontinuity Designs with Survival Outcomes
Maximilian Schuessler, Erik Sverdrup, Robert Tibshirani +1
Quasi-experimental evaluations are central for generating real-world causal evidence and complementing insights from randomized trials. The regression discontinuity design (RDD) is…
Lassoed Forests: Random Forests with Adaptive Lasso Post-selection
Jing Shang, James Bannon, Benjamin Haibe-Kains +1
Random forests are a statistical learning technique that use bootstrap aggregation to average high-variance and low-bias trees. Improvements to random forests, such as applying Las…
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…
Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction
Maximilian Schuessler, Erik Sverdrup, Robert Tibshirani
Robust estimation of heterogeneous treatment effects is a fundamental challenge for optimal decision-making in domains ranging from personalized medicine to educational policy. In…
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)…