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

Hailin Huang; Jizi Shangguan; Peifeng Ruan; Hua Liang

doi:10.1515/sagmb-2019-0016

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

Hailin Huang, Jizi Shangguan, Peifeng Ruan, Hua Liang

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Article number	20190016
Journal	Statistical Applications in Genetics and Molecular Biology
DOIs	https://doi.org/10.1515/sagmb-2019-0016
State	Accepted/In press - 2019
Externally published	Yes

Keywords

accelerated failure time (AFT) models
group selection
individual feature selection
single-index models

ASJC Scopus subject areas

Statistics and Probability
Molecular Biology
Genetics
Computational Mathematics

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10.1515/sagmb-2019-0016

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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.",

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author = "Hailin Huang and Jizi Shangguan and Peifeng Ruan and Hua Liang",

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AU - Shangguan, Jizi

AU - Ruan, Peifeng

AU - Liang, Hua

PY - 2019

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AB - 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.

KW - accelerated failure time (AFT) models

KW - group selection

KW - individual feature selection

KW - single-index models

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

Abstract

Keywords

ASJC Scopus subject areas

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