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Variable Selection for Panel Count Data via Non‐Concave Penalized Estimating Function

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  • XINGWEI TONG
  • XIN HE
  • LIUQUAN SUN
  • JIANGUO SUN

Abstract

. Variable selection is an important issue in all regression analyses, and in this paper we discuss this in the context of regression analysis of panel count data. Panel count data often occur in long‐term studies that concern occurrence rate of a recurrent event, and their analysis has recently attracted a great deal of attention. However, there does not seem to exist any established approach for variable selection with respect to panel count data. For the problem, we adopt the idea behind the non‐concave penalized likelihood approach and develop a non‐concave penalized estimating function approach. The proposed methodology selects variables and estimates regression coefficients simultaneously, and an algorithm is presented for this process. We show that the proposed procedure performs as well as the oracle procedure in that it yields the estimates as if the correct submodel were known. Simulation studies are conducted for assessing the performance of the proposed approach and suggest that it works well for practical situations. An illustrative example from a cancer study is provided.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:scjsta:v:36:y:2009:i:4:p:620-635
    DOI: 10.1111/j.1467-9469.2009.00658.x
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    Cited by:

    1. 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.
    2. Haiying Wang & Yang Li & Jianguo Sun, 2015. "Focused and Model Average Estimation for Regression Analysis of Panel Count Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 732-745, September.
    3. Yang Li & Xin He & Haiying Wang & Jianguo Sun, 2016. "Regression analysis of longitudinal data with correlated censoring and observation times," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 343-362, July.
    4. Zhao, Hui & Sun, Dayu & Li, Gang & Sun, Jianguo, 2019. "Simultaneous estimation and variable selection for incomplete event history studies," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 350-361.
    5. Jie Zhou & Haixiang Zhang & Liuquan Sun & Jianguo Sun, 2017. "Joint analysis of panel count data with an informative observation process and a dependent terminal event," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 560-584, October.
    6. Xin He, 2011. "Comments on: Nonparametric inference based on panel count data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 46-47, May.
    7. Jie Zhou & Haixiang Zhang & Liuquan Sun & Jianguo Sun, 0. "Joint analysis of panel count data with an informative observation process and a dependent terminal event," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 0, pages 1-25.

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