Abstract
Iterative Sure Independent Screening (ISIS) was proposed for the problem of variable selection with ultrahigh dimensional feature space. Unfortunately, the ISIS method transforms the dimensionality of features from ultrahigh to ultra-low and may result in un-reliable inference when the number of important variables particularly is greater than the screening threshold. The proposed method has transformed the ultrahigh dimensionality of features to high dimension space in order to remedy of losing some information by ISIS method. The proposed method is compared with ISIS method by using real data and simulation. The results show this method is more efficient and more reliable than ISIS method.
DOI
10.22237/jmasm/1608553020
Recommended Citation
Uraibi, H. S. (2020). VIF-Regression Screening Ultrahigh Dimensional Feature Space. Journal of Modern Applied Statistical Methods, 19(1), eP2916. https://doi.org/10.22237/jmasm/1608553020
Included in
Applied Statistics Commons, Social and Behavioral Sciences Commons, Statistical Theory Commons
