The least absolute shrinkage and selection operator ('lasso') has been widely used in regression shrinkage and selection. We extend its application to the regression model with autoregressive errors.

The application of the lasso is espoused in high dimensional settings where only a small number of the regression coefficients are believed to be non-zero (i.e. the solution is sparse). Moreover, ...

Overview Regression explains how changes in one factor influence another with clarity.Each regression type is suited for ...

The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features ...

Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus were used to construct a 12-gene ARG-based prognostic signature through LASSO and Cox regression ...