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

Author

Listed:
  • Huang Hailin

    (Department of Statistics, George Washington University, Washington, DC 20052, USA)

  • Shangguan Jizi

    (Department of Statistics, George Washington University, Washington, DC 20052, USA)

  • Ruan Peifeng

    (Department of Statistics, George Washington University, Washington, DC 20052, USA)

  • Liang Hua

    (Department of Statistics, George Washington University, Washington, DC 20052, USA)

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.

Suggested Citation

  • Huang Hailin & Shangguan Jizi & Ruan Peifeng & Liang Hua, 2019. "Bi-level feature selection in high dimensional AFT models with applications to a genomic study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(5), pages 1-11, October.
    • Handle: RePEc:bpj:sagmbi:v:18:y:2019:i:5:p:11:n:4
    DOI: 10.1515/sagmb-2019-0016
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    References listed on IDEAS

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