Print Email Facebook Twitter High-dimensional sparse vine copula regression with application to genomic prediction Title High-dimensional sparse vine copula regression with application to genomic prediction Author Şahin, Ö. (TU Delft Applied Probability) Czado, Claudia Date 2023 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 2 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. To reference this document use: http://resolver.tudelft.nl/uuid:75f658d0-b8e9-431e-b1ac-63cc6d83195b DOI https://doi.org/10.1093/biomtc/ujad042 Edition 1 Volume 80 Series Biometrics Part of collection Institutional Repository Document type other Rights © 2023 Ö. Şahin, Claudia Czado Files PDF ujad042.pdf 914.8 KB Close viewer /islandora/object/uuid:75f658d0-b8e9-431e-b1ac-63cc6d83195b/datastream/OBJ/view