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pro vyhledávání: '"Feser, Fabio"'
Autor:
Feser, Fabio, Evangelou, Marina
The sparse-group lasso performs both variable and group selection, making simultaneous use of the strengths of the lasso and group lasso. It has found widespread use in genetics, a field that regularly involves the analysis of high-dimensional data,
Externí odkaz:
http://arxiv.org/abs/2405.17094
Autor:
Feser, Fabio, Evangelou, Marina
Tuning the regularization parameter in penalized regression models is an expensive task, requiring multiple models to be fit along a path of parameters. Strong screening rules drastically reduce computational costs by lowering the dimensionality of t
Externí odkaz:
http://arxiv.org/abs/2405.15357
Autor:
Feser, Fabio, Evangelou, Marina
In this manuscript, a new high-dimensional approach for simultaneous variable and group selection is proposed, called sparse-group SLOPE (SGS). SGS achieves false discovery rate control at both variable and group levels by incorporating the SLOPE mod
Externí odkaz:
http://arxiv.org/abs/2305.09467