3 papers
stat.ME2025
Penalized mixed models to adjust for batch effects and unobserved confounding in high dimensional regression
Yujing Lu, Patrick Breheny
Confounding can lead to spurious associations. Typically, one must observe confounders in order to adjust for them, but in high-dimensional settings, recent research has shown that…
stat.ME2025
Cross-Validation in Penalized Linear Mixed Models: Addressing Common Implementation Pitfalls
Tabitha K. Peter, Patrick J. Breheny
In this paper, we develop an implementation of cross-validation for penalized linear mixed models. While these models have been proposed for correlated high-dimensional data, the c…
stat.CO2025
plmmr: an R package to fit penalized linear mixed models for genome-wide association data with complex correlation structure
Tabitha K. Peter, Anna C. Reisetter, Yujing Lu +2
Correlation among the observations in high-dimensional regression modeling can be a major source of confounding. We present a new open-source package, plmmr, to implement penalized…