1 citations · 1 across the 3 of their papers we have counts for
7 papers
Virtual Dummies: Enabling Scalable FDR-Controlled Variable Selection via Sequential Sampling of Null Features
Taulant Koka, Jasin Machkour, Daniel P. Palomar +1
High-dimensional variable selection, particularly in genomics, requires error-controlling procedures that scale to millions of predictors. The Terminating-Random Experiments (T-Rex…
FDR Control for Complex-Valued Data with Application in Single Snapshot Multi-Source Detection and DOA Estimation
Fabian Scheidt, Jasin Machkour, Michael Muma
False discovery rate (FDR) control is a popular approach for maintaining the integrity of statistical analyses, especially in high-dimensional data settings, where multiple compari…
Learning False Discovery Rate Control via Model-Based Neural Networks
Arnau Vilella, Jasin Machkour, Michael Muma +1
Controlling the false discovery rate (FDR) in high-dimensional variable selection requires balancing rigorous error control with statistical power. Existing methods with provable g…
Reproducible Physiological Features in Affective Computing: A Preliminary Analysis on Arousal Modeling
Andrea Gargano, Jasin Machkour, Mimma Nardelli +2
In Affective Computing, a key challenge lies in reliably linking subjective emotional experiences with objective physiological markers. This preliminary study addresses the issue o…
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…