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 ...
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 ...
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, ...
Marketers must be deliberate when adding dimensions to a machine learning model. The cost of adding too many is accuracy. Decluttering fever is sweeping the country thanks to Marie Kondo. But clutter ...
Conventional dimension reduction methods deal mainly with simple data structure and are inappropriate for data with matrix-valued predictors. Li, Kim, and Altman (2010) proposed dimension folding ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback