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Variable selection in the additive rate model for recurrent event data


  • Chen, Xiaolin
  • Wang, Qihua


In this paper, we investigate the variable selection problem for recurrent event data under the additive rate model. According to the explicit estimator of the regression coefficients of the additive rate model, a loss function is constructed. It has a form similar to the ordinary least squares of a linear regression model up to a constant. We develop variable selection procedures by penalizing the loss function with the adaptive L1 penalty and smoothly clipped absolute derivation penalty, respectively. Under some mild regularity conditions, the oracle properties of both procedures are established. Extensive simulation studies are conducted to examine the performance of our proposed procedures in finite samples. Finally, these methods are applied to the well-known chronic granulomatous disease study.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:491-503 DOI: 10.1016/j.csda.2012.06.019

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    References listed on IDEAS

    1. 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.
    2. Leiva, Victor & Barros, Michelli & Paula, Gilberto A. & Galea, Manuel, 2007. "Influence diagnostics in log-Birnbaum-Saunders regression models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5694-5707, August.
    3. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. 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.
    6. Torben Martinussen & Thomas H. Scheike, 2009. "Covariate Selection for the Semiparametric Additive Risk Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 602-619.
    7. Hao Helen Zhang & Wenbin Lu, 2007. "Adaptive Lasso for Cox's proportional hazards model," Biometrika, Biometrika Trust, vol. 94(3), pages 691-703.
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    Cited by:

    1. Zhao, Xiaobing & Zhou, Xian, 2014. "Sufficient dimension reduction on marginal regression for gaps of recurrent events," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 56-71.


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