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Adaptive group bridge selection in the semiparametric accelerated failure time model

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  • Huang, Longlong
  • Kopciuk, Karen
  • Lu, Xuewen

Abstract

The group bridge penalized method has been studied in the multiple linear regression model and the semiparametric accelerated failure time (AFT) model and demonstrated the capability to remove unimportant groups, however, it cannot effectively remove unimportant variables within the important groups. To overcome this limitation, we propose the adaptive group bridge method in the AFT model. We show that the adaptive group bridge method enjoys the powerful oracle property. Simulation studies indicate that the adaptive group bridge approach for the AFT model can correctly identify both important groups and important within-group individual variables even with high censoring rates in high-dimensional data. The PBC data is analyzed to illustrate the application of the proposed method.

Suggested Citation

  • Huang, Longlong & Kopciuk, Karen & Lu, Xuewen, 2020. "Adaptive group bridge selection in the semiparametric accelerated failure time model," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:jmvana:v:175:y:2020:i:c:s0047259x19302970
    DOI: 10.1016/j.jmva.2019.104562
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    References listed on IDEAS

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    1. Jiahua Chen & Zehua Chen, 2008. "Extended Bayesian information criteria for model selection with large model spaces," Biometrika, Biometrika Trust, vol. 95(3), pages 759-771.
    2. Jian Huang & Shuange Ma & Huiliang Xie & Cun-Hui Zhang, 2009. "A group bridge approach for variable selection," Biometrika, Biometrika Trust, vol. 96(2), pages 339-355.
    3. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    4. S. Wang & B. Nan & N. Zhu & J. Zhu, 2009. "Hierarchically penalized Cox regression with grouped variables," Biometrika, Biometrika Trust, vol. 96(2), pages 307-322.
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    Cited by:

    1. Wenjing Yin & Sihai Dave Zhao & Feng Liang, 2022. "Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 282-318, April.

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