# Vine copula based knockoff generation for high-dimensional controlled variable selection

arXiv: https://arxiv.org/abs/2210.11196 · HTML: https://mlsanity.com/paper/2210.11196/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2022.

## Built on by (most cited first, 1 of 1)

- [Knockoffs-based False Discovery Rate Control and Simplification for Deep Neural Networks](https://mlsanity.com/paper/2606.04404/) (2026)

## Abstract (arXiv)

Vine copulas are a flexible tool for high-dimensional dependence modeling. In this article, we discuss the generation of approximate model-X knockoffs with vine copulas. It is shown how Gaussian knockoffs can be generalized to Gaussian copula knockoffs. A convenient way to parametrize Gaussian copulas are partial correlation vines. We discuss how completion problems for partial correlation vines are related to Gaussian knockoffs. A natural generalization of partial correlation vines are vine copulas which are well suited for the generation of approximate model-X knockoffs. We discuss a specific D-vine structure which is advantageous to obtain vine copula knockoff models. In a simulation study, we demonstrate that vine copula knockoff models are effective and powerful for high-dimensional controlled variable selection.