675 citations · 1.4k across the 12 of their papers we have counts for
13 papers
A Novel Approach to Instrumental Variable Estimation: TEAM-IV
Steven Y. Alberding, Patrick J. Breheny
Instrumental-variable (IV) analyses can be undermined when some instruments violate the exclusion restriction through direct effects on the outcome. Existing robust IV methods, inc…
Cross validation approaches for penalized Cox regression
Biyue Dai, Patrick Breheny
Cross validation is commonly used for selecting tuning parameters in penalized regression, but its use in penalized Cox regression models has received relatively little attention i…
Feature-specific inference for penalized regression using local false discovery rates
Ryan Miller, Patrick Breheny
Penalized regression methods, most notably the lasso, are a popular approach to analyzing high-dimensional data. An attractive property of the lasso is that it naturally performs v…
Marginal false discovery rate control for likelihood-based penalized regression models
Ryan Miller, Patrick Breheny
The popularity of penalized regression in high-dimensional data analysis has led to a demand for new inferential tools for these models. False discovery rate control is widely used…
Hybrid safe-strong rules for efficient optimization in lasso-type problems
Yaohui Zeng, Tianbao Yang, Patrick Breheny
The lasso model has been widely used for model selection in data mining, machine learning, and high-dimensional statistical analysis. However, with the ultrahigh-dimensional, large…
The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R
Yaohui Zeng, Patrick Breheny
Penalized regression models such as the lasso have been extensively applied to analyzing high-dimensional data sets. However, due to memory limitations, existing R packages like gl…