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**Variable selection for Cox’s proportional hazards model and frailty model.**
*(English)*
Zbl 1012.62106

Summary: A class of variable selection procedures for parametric models via nonconcave penalized likelihood was proposed by J. Fan and R. Li [J. Am. Stat. Assoc. 96, No. 456, 1348-1360 (2001)]. It has been shown there that the resulting procedures perform as well as if the subset of significant variables were known in advance. Such a property is called an oracle property. The proposed procedures were illustrated in the context of linear regression, robust linear regression and generalized linear models. In this paper, the nonconcave penalized likelihood approach is extended further to the Cox proportional hazards model and the Cox proportional hazards frailty model, two commonly used semi-parametric models in survival analysis. As a result, new variable selection procedures for these two commonly-used models are proposed.

It is demonstrated how the rates of convergence depend on the regularization parameter in the penalty function. Further, with a proper choice of the regularization parameter and the penalty function, the proposed estimators possess an oracle property. Standard error formulae are derived and their accuracies are empirically tested. Simulation studies show that the proposed procedures are more stable in prediction and more effective in computation than the best subset variable selection, and they reduce model complexity as effectively as the best subset variable selection. Compared with the LASSO, which is the penalized likelihood method with the \(L_1\) -penalty, proposed by R. Tibshirani [J. R. Stat. Soc., Ser. B 58, No. 1, 267-288 (1996)], the newly proposed approaches have better theoretic properties and finite sample performance.

It is demonstrated how the rates of convergence depend on the regularization parameter in the penalty function. Further, with a proper choice of the regularization parameter and the penalty function, the proposed estimators possess an oracle property. Standard error formulae are derived and their accuracies are empirically tested. Simulation studies show that the proposed procedures are more stable in prediction and more effective in computation than the best subset variable selection, and they reduce model complexity as effectively as the best subset variable selection. Compared with the LASSO, which is the penalized likelihood method with the \(L_1\) -penalty, proposed by R. Tibshirani [J. R. Stat. Soc., Ser. B 58, No. 1, 267-288 (1996)], the newly proposed approaches have better theoretic properties and finite sample performance.

### MSC:

62N02 | Estimation in survival analysis and censored data |

62F12 | Asymptotic properties of parametric estimators |

62M99 | Inference from stochastic processes |

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\textit{J. Fan} and \textit{R. Li}, Ann. Stat. 30, No. 1, 74--99 (2002; Zbl 1012.62106)

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### References:

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[28] | SHATIN, HONG KONG E-MAIL: jfan@sta.cuhk.edu.hk DEPARTMENT OF STATISTICS PENNSYLVANIA STATE UNIVERSITY UNIVERSITY PARK, PENNSYLVANIA 16802-2111 E-MAIL: rli@stat.psu.edu |

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