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Dokumenttyp:
Zeitschriftenaufsatz 
Autor(en):
Sahin, Özge; Czado, Claudia 
Titel:
High-dimensional sparse vine copula regression with application to genomic prediction 
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 computationa...    »
 
Dewey Dezimalklassifikation:
510 Mathematik 
Zeitschriftentitel:
Preprint 
Jahr:
2022 
Sprache:
en 
Verlag / Institution:
arXiv 
Semester:
SS 22 
TUM Einrichtung:
Professur für Angewandte Mathematische Statistik 
Format:
Text