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Found 5 papers, 0 papers with code
High-Dimensional False Discovery Rate Control for Dependent Variables
In recent years, multivariate false discovery rate (FDR) controlling methods have emerged, providing guarantees even in high-dimensional settings where the number of variables surpasses the number of samples.
FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking
In high-dimensional data analysis, such as financial index tracking or biomedical applications, it is crucial to select the few relevant variables while maintaining control over the false discovery rate (FDR).
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening
Unfortunately, well-established estimators, such as the graphical lasso or neighborhood selection, are known to be susceptible to a high prevalence of false edge detections.
Sparse PCA with False Discovery Rate Controlled Variable Selection
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension.
The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control
The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables.
