High-dimensional sparse vine copula regression with application to genomic prediction

TL;DR

This paper introduces scalable sparse vine copula regression methods tailored for high-dimensional genomic data, demonstrating improved variable selection and prediction accuracy over existing models through simulations and real maize trait prediction.

Contribution

The paper develops two novel methods for high-dimensional sparse vine copula regression, enhancing computational efficiency and variable selection in genomic prediction tasks.

Findings

Methods outperform existing approaches in simulation studies.

Proposed models achieve higher prediction accuracy in real data.

Effective variable selection improves model interpretability.

Abstract

High-dimensional data sets are often available in genome-enabled predictions. Such data sets include nonlinear relationships with complex dependence structures. For such situations, vine copula based (quantile) regression is an important tool. However, the current vine copula based regression approaches do not scale up to high and ultra-high dimensions. To perform high-dimensional sparse vine copula based regression, we propose two methods. First, we show their superiority regarding computational complexity over the existing methods. Second, we define relevant, irrelevant, and redundant explanatory variables for quantile regression. Then we show our method's power in selecting relevant variables and prediction accuracy in high-dimensional sparse data sets via simulation studies. Next, we apply the proposed methods to the high-dimensional real data, aiming at the genomic prediction of maize traits. Some data-processing and feature extraction steps for the real data are further discussed. Finally, we show the advantage of our methods over linear models and quantile regression forests in simulation studies and real data applications.