The progress in science and engineering increasingly depends on our ability to analyze massive amounts of observed and simulated data. The vast majority of this data, coming from high-performance high ...

Motivated by a study conducted to evaluate the associations of 51 inflammatory markers and lung cancer risk, we propose several approaches of varying computational complexity for analyzing multiple ...

High-dimensional -omics data such as genomic, transcriptomic, and metabolomic data offer great promise in advancing precision medicine. In particular, such data have enabled the investigation of ...

Sufficient dimension reduction often resorts to inverse regression, and most inverse regression methods rely on slicing a quantitative response. The choice of a particular slicing scheme is critical, ...

This study aims to improve survival modeling in head and neck cancer (HNC) by integrating patient-reported outcomes (PROs) using dimensionality reduction techniques. PROs capture symptom severity ...