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Adaptive LASSO for general transformation models with right censored data

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  • Li, Jianbo
  • Gu, Minggao

Abstract

In this paper, we consider variable selection for general transformation models with right censored data and propose a unified procedure for both variable selection and estimation. We conduct the proposed procedure by maximizing penalized log-marginal likelihood function with Adaptive LASSO penalty (ALASSO) on regression coefficients. Two main advantages of this procedure are as follows: (i) the penalties can be assigned to regression coefficients adaptively by data according to the importance of corresponding covariates; (ii) it is free of baseline survival function and censoring distribution. Under some regular conditions, we show that the penalized estimates with ALASSO are n-consistent and enjoy oracle properties. Some simulation examples and Primary Biliary Cirrhosis Data application illustrate that our proposed procedure works very well for moderate sample size.

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  • Li, Jianbo & Gu, Minggao, 2012. "Adaptive LASSO for general transformation models with right censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2583-2597.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:8:p:2583-2597
    DOI: 10.1016/j.csda.2012.02.023
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    References listed on IDEAS

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    Cited by:

    1. Wenyan Zhong & Xuewen Lu & Jingjing Wu, 2021. "Bi-level variable selection in semiparametric transformation models with right-censored data," Computational Statistics, Springer, vol. 36(3), pages 1661-1692, September.
    2. Li, Jianbo & Gu, Minggao & Zhang, Riquan, 2013. "Variable selection for general transformation models with right censored data via nonconcave penalties," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 445-456.
    3. Chen, Xiaolin & Wang, Qihua, 2013. "Variable selection in the additive rate model for recurrent event data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 491-503.

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