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June 2020 Bayesian variable selection for survival data using inverse moment priors
Amir Nikooienejad, Wenyi Wang, Valen E. Johnson
Ann. Appl. Stat. 14(2): 809-828 (June 2020). DOI: 10.1214/20-AOAS1325

Abstract

Efficient variable selection in high-dimensional cancer genomic studies is critical for discovering genes associated with specific cancer types and for predicting response to treatment. Censored survival data is prevalent in such studies. In this article we introduce a Bayesian variable selection procedure that uses a mixture prior composed of a point mass at zero and an inverse moment prior in conjunction with the partial likelihood defined by the Cox proportional hazard model. The procedure is implemented in the R package BVSNLP, which supports parallel computing and uses a stochastic search method to explore the model space. Bayesian model averaging is used for prediction. The proposed algorithm provides better performance than other variable selection procedures in simulation studies and appears to provide more consistent variable selection when applied to actual genomic datasets.

Citation

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Amir Nikooienejad. Wenyi Wang. Valen E. Johnson. "Bayesian variable selection for survival data using inverse moment priors." Ann. Appl. Stat. 14 (2) 809 - 828, June 2020. https://doi.org/10.1214/20-AOAS1325

Information

Received: 1 June 2018; Revised: 1 January 2020; Published: June 2020
First available in Project Euclid: 29 June 2020

zbMATH: 07239885
MathSciNet: MR4117831
Digital Object Identifier: 10.1214/20-AOAS1325

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 2 • June 2020
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