Abstract
Recurrent events data with a terminal event (e.g., death) often arise in clinical and observational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.
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References
M. C. Wang, J. Qin, and C. T. Chiang, Analyzing recurrent event data with informative censoring, J. Amer. Statist. Assoc., 2001, 96: 1057–1065.
C. Y. Huang and M. C. Wang, Joint modeling and estimation for recurrent event processes and failure time data, J. Amer. Statist. Assoc., 2004, 99: 1153–1165.
D. Y. Lin, L. J. Wei, I. Yang, and Z. Ying, Semiparametric regression for the mean and rate function of recurrent events, J. R. Stat. Soc. B., 2000, 69: 711–730.
H. Akaike, Maximum likelihood identification of Gaussian autoregressive moving average models, Biometrika, 1973, 60: 255–265.
C. L. Mallows, Some comments on C p, Technometrics, 1973, 15: 661–675.
G. Schwartz, Estimating the dimension of a model, Ann. Stat., 1978, 6: 461–464.
L. Breiman, Heuristics of instability and stabilization in model selection, Ann. Stat., 1996, 24: 2350–2383.
J. Fan and H. Peng, Nonconcave penalized likelihood with a diverging number of parameters, Ann. Stat., 2004, 32: 928–961.
J. Fan and R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Am. Stat. Assoc., 2001, 96: 1348–1360.
R. J. Tibshirani, Regression shrinkage and selection via the Lasso, J. R. Stat. Soc. B., 1996, 58: 267–288.
R. J. Tibshirani, The Lasso method for variable selection in the Cox model, Stat. Med., 1997, 16: 385–395.
K. Knight and W. Fu, Asymptotics for Lasso-type estimators, Ann. Stat., 2000, 28: 1356–1378.
A. Antoniadis, Wavelets in statistics: A review (with discussion), J. Italian Stat. Soc., 1997, 6: 97–144.
J. Fan, Comment on ‘Wavelets in statistics: A review’ by A. Antoniadis, J. Italian Stat. Soc., 1997, 6: 131–138.
X. Tong, L. Zhu, and J. G. Sun, Variable selection for recurrent event data via nonconcave penalized estimating function, Lifetime data Ana., 2009, 15: 197–215.
X. Tong, X. He, L. Q. Sun, and J. G. Sun, Variable selection for panel count data via nonconcave penalized estimating function, Scand. J. Stat., 2009, 36: 620–635.
J. Cai and D. E. Schaubel, Marginal means/rates models for multiple type recurrent event data, Lifetime Data Anal, 2004, 10: 121–138.
J. Cai, J. Fan, R. Li, and H. Zhou, Variable selection for multivariate failure time data, Biometrika, 2005, 92: 303–316.
R. Li and H. Liang, Variable selection in semiparametric regression modeling, Ann. Stat., 2008, 6: 261–286.
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This paper was recommended for publication by Editor Guohua ZOU.
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Cheng, X., Luo, L. Variable selection for recurrent event data with informative censoring. J Syst Sci Complex 25, 987–997 (2012). https://doi.org/10.1007/s11424-012-1098-x
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DOI: https://doi.org/10.1007/s11424-012-1098-x