Author:
Huang Hailin,Shangguan Jizi,Ruan Peifeng,Liang Hua
Abstract
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.
Subject
Computational Mathematics,Genetics,Molecular Biology,Statistics and Probability
Cited by
1 articles.
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