A selective overview of feature screening for ultrahigh-dimensional data

Science China. Mathematics
Liu JingYuanL I RunZe

Abstract

High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of high-dimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many high-dimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahi...Continue Reading

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Citations

Feb 28, 2017·The Science of the Total Environment· Jaiprakash, Gazala Habib
Oct 5, 2017·SAR and QSAR in Environmental Research·A M Al-FakihM Aziz
Aug 18, 2020·The Annals of Applied Statistics·Wanghuan ChuMatthew Reimherr

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