Robust feature screening for high-dimensional survival data
Robust feature screening for high-dimensional survival data is a scholarly work, published in 2018 in ''Journal of Applied Statistics''. The main subjects of the publication include regularization, data mining, dimension, artificial intelligence, Clustering high-dimensional data, curse of dimensionality, computation, feature, RNA splicing, colorectal cancer, consistency, machine learning, feature selection, and computer science. The authors introduce a simple and robust feature screening method without any model assumption to tackle high dimensional censored data.