Many contemporary large-scale applications involve building interpretable models linking a
large set of potential covariates to a response in a non-linear fashion, such as when the …

Abstract Model-X knockoffs is a flexible wrapper method for high-dimensional regression
algorithms, which provides guaranteed control of the false discovery rate (FDR). Due to the …

Power and reproducibility are key to enabling refined scientific discoveries in contemporary
big data applications with general high-dimensional nonlinear models. In this article, we …

We consider the problem of variable selection in high-dimensional statistical models where
the goal is to report a set of variables, out of many predictors X_1,\dotsc,X_p, that are …

We review variable selection and variable screening in high-dimensional linear models.
Thereby, a major focus is an empirical comparison of various estimation methods with …

We propose a methodology for testing linear hypothesis in high-dimensional linear models.
The proposed test does not impose any restriction on the size of the model, that is, model …

We consider the variable selection problem, which seeks to identify important variables
influencing a response Y out of many candidate features X ₁,..., Xp. We wish to do so while …

Assigning significance in high-dimensional regression is challenging. Most computationally
efficient selection algorithms cannot guard against inclusion of noise variables …

Interpretability and stability are two important features that are desired in many
contemporary big data applications arising in statistics, economics, and finance. While the …

Abstract Model-X knockoffs is a wrapper that transforms essentially any feature importance
measure into a variable selection algorithm, which discovers true effects while rigorously …