390 citations · 3.4k across the 56 of their papers we have counts for
60 papers
Joint Activity Detection and Channel Estimation for Massive Random Access Using SBL and SCA
Esa Ollila, Majdoddin Esfandiari, Daniel P. Palomar
In massive machine-type communication (mMTC) applications, a key challenge is joint device activity detection and channel estimation (JADCE) under grant-free random access, as a ma…
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…
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…
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…