5 citations · 7 across the 7 of their papers we have counts for
7 papers
The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research
Jasin Machkour, Michael Muma, Daniel P. Palomar
Modern genomics research relies on genome-wide association studies (GWAS) to identify the few genetic variants among potentially millions that are associated with diseases of inter…
False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks
Jasin Machkour, Michael Muma, Daniel P. Palomar
Genomics biobanks are information treasure troves with thousands of phenotypes (e.g., diseases, traits) and millions of single nucleotide polymorphisms (SNPs). The development of m…
Solving FDR-Controlled Sparse Regression Problems with Five Million Variables on a Laptop
Fabian Scheidt, Jasin Machkour, Michael Muma
Currently, there is an urgent demand for scalable multivariate and high-dimensional false discovery rate (FDR)-controlling variable selection methods to ensure the repro-ducibility…
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…