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Variable selection in semiparametric transformation models for right-censored data

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  • Xiaoxi Liu
  • Donglin Zeng

Abstract

We study variable selection in general transformation models for right-censored data. The models studied can incorporate external time-varying covariates, and they include the proportional hazards model and the proportional odds model as special cases. We propose an estimation method that involves minimizing a weighted negative partial loglikelihood function plus an adaptive lasso penalty, with the initial values obtained from nonparametric maximum likelihood estimation. The objective function is parametric and convex, so the minimization is easy to implement. We show that our selection has oracle properties and that the estimator is semiparametrically efficient. We demonstrate the small-sample performance of the proposed method via simulations, and we use the method to analyse data from the Atherosclerosis Risk in Communities Study. Copyright 2013, Oxford University Press.

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  • Xiaoxi Liu & Donglin Zeng, 2013. "Variable selection in semiparametric transformation models for right-censored data," Biometrika, Biometrika Trust, vol. 100(4), pages 859-876.
    • Handle: RePEc:oup:biomet:v:100:y:2013:i:4:p:859-876
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    File URL: http://hdl.handle.net/10.1093/biomet/ast029
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    Cited by:

    1. Tian Tian & Jianguo Sun, 2024. "Variable Selection for Nonlinear Covariate Effects with Interval-Censored Failure Time Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(1), pages 185-202, April.
    2. Liuquan Sun & Shuwei Li & Lianming Wang & Xinyuan Song & Xuemei Sui, 2022. "Simultaneous variable selection in regression analysis of multivariate interval‐censored data," Biometrics, The International Biometric Society, vol. 78(4), pages 1402-1413, December.
    3. Huaihou Chen & Donglin Zeng & Yuanjia Wang, 2017. "Penalized nonlinear mixed effects model to identify biomarkers that predict disease progression," Biometrics, The International Biometric Society, vol. 73(4), pages 1343-1354, December.
    4. Lu, Huimin & Wang, Yilong & Bing, Heming & Wang, Shuying & Li, Niya, 2025. "Efficient regularized estimation of graphical proportional hazards model with interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 209(C).
    5. Mingzhe Wu & Ming Zheng & Wen Yu & Ruofan Wu, 2018. "Estimation and variable selection for semiparametric transformation models under a more efficient cohort sampling design," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 570-596, September.
    6. 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.
    7. Lou, Yichen & Ma, Yuqing & Xiang, Liming & Sun, Jianguo, 2025. "A multiple imputation approach for flexible modelling of interval-censored data with missing and censored covariates," Computational Statistics & Data Analysis, Elsevier, vol. 209(C).
    8. Xi Ning & Yinghao Pan & Yanqing Sun & Peter B. Gilbert, 2023. "A semiparametric Cox–Aalen transformation model with censored data," Biometrics, The International Biometric Society, vol. 79(4), pages 3111-3125, December.
    9. Du, Mingyue & Zhao, Xingqiu & Sun, Jianguo, 2022. "Variable selection for case-cohort studies with informatively interval-censored outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    10. Qu, Lianqiang & Song, Xinyuan & Sun, Liuquan, 2018. "Identification of local sparsity and variable selection for varying coefficient additive hazards models," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 119-135.

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