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 ...
Scandinavian Journal of Statistics, Vol. 45, No. 2 (June 2018), pp. 217-254 (38 pages) Non-parametric generalized likelihood ratio test is a popular method of model checking for regressions. However, ...
Scientists can now monitor and record the activity of hundreds of neurons concurrently in the brain, and ongoing technology developments promise to increase this number. However, simply recording the ...
Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation of a dataset that has fewer columns. Imagine that you have a dataset that has many ...
In this article, we consider sufficient dimension folding for the regression mean function when predictors are matrix- or array-valued. We propose a new concept named central mean dimension folding ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback