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Robust variable selection of joint frailty model for panel count data

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  • Wang, Weiwei
  • Wu, Xianyi
  • Zhao, Xiaobing
  • Zhou, Xian

Abstract

Panel count data are generated from studies that concern recurrent events or event history studies in which the subjects are observed only at specific points in time. Recently, research on panel count data has drawn considerable attention. The literature on variable selection of panel count data has so far been quite limited. In this paper, a robust variable selection approach based on the quantile regression function in a joint frailty model is proposed to analyze panel count data. A three-step estimation method is introduced to estimate the coefficients and unknown functions. Consistency and oracle properties are established under some mild regularity conditions. Simulations are used to assess the proposed estimation method. Bladder tumor cancer data are also re-analyzed as an illustration.

Suggested Citation

  • Wang, Weiwei & Wu, Xianyi & Zhao, Xiaobing & Zhou, Xian, 2018. "Robust variable selection of joint frailty model for panel count data," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 60-78.
    • Handle: RePEc:eee:jmvana:v:167:y:2018:i:c:p:60-78
    DOI: 10.1016/j.jmva.2018.04.003
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    References listed on IDEAS

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    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. Xiaobing Zhao & Jinglong Wang & Xian Zhou & Zhongyi Zhu, 2015. "Recurrent Events Analysis in the Presence of Terminal Event and Zero-recurrence Subjects," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(4), pages 710-725, February.
    3. Lu, Minggen & Zhang, Ying & Huang, Jian, 2009. "Semiparametric Estimation Methods for Panel Count Data Using Monotone B-Splines," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1060-1070.
    4. Duncan Lee & Tereza Neocleous, 2010. "Bayesian quantile regression for count data with application to environmental epidemiology," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 905-920, November.
    5. Chiung-Yu Huang & Mei-Cheng Wang, 2004. "Joint Modeling and Estimation for Recurrent Event Processes and Failure Time Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1153-1165, December.
    6. Yingcun Xia & Howell Tong & W. K. Li & Li‐Xing Zhu, 2002. "An adaptive estimation of dimension reduction space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 363-410, August.
    7. J. D. Nielsen & C. B. Dean, 2008. "Clustered Mixed Nonhomogeneous Poisson Process Spline Models for the Analysis of Recurrent Event Panel Data," Biometrics, The International Biometric Society, vol. 64(3), pages 751-761, September.
    8. 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.
    9. Machado, Jose A.F. & Silva, J. M. C. Santos, 2005. "Quantiles for Counts," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1226-1237, December.
    10. Wang, Hansheng & Xia, Yingcun, 2009. "Shrinkage Estimation of the Varying Coefficient Model," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 747-757.
    11. Zhao, Xingqiu & Tong, Xingwei, 2011. "Semiparametric regression analysis of panel count data with informative observation times," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 291-300, January.
    12. Howard D. Bondell & Arun Krishna & Sujit K. Ghosh, 2010. "Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models," Biometrics, The International Biometric Society, vol. 66(4), pages 1069-1077, December.
    13. Hui Zhao & Yang Li & Jianguo Sun, 2013. "Semiparametric analysis of multivariate panel count data with dependent observation processes and a terminal event," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 379-394, June.
    14. Xingwei Tong & Xin He & Liuquan Sun & Jianguo Sun, 2009. "Variable Selection for Panel Count Data via Non‐Concave Penalized Estimating Function," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 620-635, December.
    15. Guo, Jie & Tang, Manlai & Tian, Maozai & Zhu, Kai, 2013. "Variable selection in high-dimensional partially linear additive models for composite quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 56-67.
    16. Zhao, Xingqiu & Tong, Xingwei & Sun, Jianguo, 2013. "Robust estimation for panel count data with informative observation times," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 33-40.
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    Cited by:

    1. Dongxiao Han & Xiaogang Su & Liuquan Sun & Zhou Zhang & Lei Liu, 2020. "Variable selection in joint frailty models of recurrent and terminal events," Biometrics, The International Biometric Society, vol. 76(4), pages 1330-1339, December.

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