1 citations · 2 across the 5 of their papers we have counts for
5 papers
High-Dimensional False Discovery Rate Control for Dependent Variables
Jasin Machkour, Michael Muma, Daniel P. Palomar
Algorithms that ensure reproducible findings from large-scale, high-dimensional data are pivotal in numerous signal processing applications. In recent years, multivariate false dis…
FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking
Jasin Machkour, Daniel P. Palomar, Michael Muma
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 th…
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening
Taulant Koka, Jasin Machkour, Michael Muma
Gaussian graphical models emerge in a wide range of fields. They model the statistical relationships between variables as a graph, where an edge between two variables indicates con…
Sparse PCA with False Discovery Rate Controlled Variable Selection
Jasin Machkour, Arnaud Breloy, Michael Muma +2
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension. By imposing loading vectors to be sparse, it performs the…
Identifying the Complete Correlation Structure in Large-Scale High-Dimensional Data Sets with Local False Discovery Rates
Martin Gölz, Tanuj Hasija, Michael Muma +1
The identification of the dependent components in multiple data sets is a fundamental problem in many practical applications. The challenge in these applications is that often the…