The accelerated failure time model always offers a valuable complement to the traditional Cox proportional hazards model due to its direct and meaningful interpretation.We propose a variable selection method in the co...The accelerated failure time model always offers a valuable complement to the traditional Cox proportional hazards model due to its direct and meaningful interpretation.We propose a variable selection method in the context of the accelerated failure time model for survival data,which can simultaneously complete variable selection and parameter estimation.Meanwhile,the proposed method can deal with the potential outliers in survival times as well as heteroscedastic model errors,which are frequently encountered in practice.Specifically,utilizing the general nonconvex penalty,we propose the adaptive penalized weighted least absolute deviation estimator for the accelerated failure time model.Under some regularity conditions,we show that the proposed method yields consistent estimator and possesses the oracle property.In addition,we propose a new algorithm to compute the estimate in the high dimensional settings,and evaluate the practical utility of the proposed method through extensive simulation studies and two real examples.展开更多
基金Supported by the National Natural Science Foundation of China(Grant Nos.11671311,11771250 and 11971324)the Natural Science Foundation of Shandong Province(Grant No.ZR2019MA002)the National Key Research and Development Program of China(Grant No.2018YFC1314600)。
文摘The accelerated failure time model always offers a valuable complement to the traditional Cox proportional hazards model due to its direct and meaningful interpretation.We propose a variable selection method in the context of the accelerated failure time model for survival data,which can simultaneously complete variable selection and parameter estimation.Meanwhile,the proposed method can deal with the potential outliers in survival times as well as heteroscedastic model errors,which are frequently encountered in practice.Specifically,utilizing the general nonconvex penalty,we propose the adaptive penalized weighted least absolute deviation estimator for the accelerated failure time model.Under some regularity conditions,we show that the proposed method yields consistent estimator and possesses the oracle property.In addition,we propose a new algorithm to compute the estimate in the high dimensional settings,and evaluate the practical utility of the proposed method through extensive simulation studies and two real examples.
