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Bi-level feature selection in high dimensional AFT models with applications to a genomic study

Huang Hailin, Shangguan Jizi, Ruan Peifeng and Liang Hua ()
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Liang Hua: Department of Statistics, George Washington University, Washington, DC 20052, USA

Statistical Applications in Genetics and Molecular Biology, 2019, vol. 18, issue 5, 11

Abstract: We propose a new bi-level feature selection method for high dimensional accelerated failure time models by formulating the models to a single index model. The method yields sparse solutions at both the group and individual feature levels along with an expedient algorithm, which is computationally efficient and easily implemented. We analyze a genomic dataset for an illustration, and present a simulation study to show the finite sample performance of the proposed method.

Keywords: accelerated failure time (AFT) models; group selection; individual feature selection; single-index models (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/sagmb-2019-0016

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