Jackknife empirical likelihood inference for the accelerated failure time model

被引:0
作者
Xue Yu
Yichuan Zhao
机构
[1] Georgia State University,Department of Mathematics and Statistics
来源
TEST | 2019年 / 28卷
关键词
Accelerated failure time model; Empirical likelihood; Jackknife; 62G20; 62N01; 62N03;
D O I
暂无
中图分类号
学科分类号
摘要
Accelerated failure time (AFT) model is a useful semi-parametric model under right censoring, which is an alternative to the commonly used proportional hazards model. Making statistical inference for the AFT model has attracted considerable attention. However, it is difficult to compute the estimators of regression parameters due to the lack of smoothness for rank-based estimating equations. Brown and Wang (Stat Med 26(4):828–836, 2007) used an induced smoothing approach, which smooths the estimating functions to obtain point and variance estimators. In this paper, a more computationally efficient method called jackknife empirical likelihood (JEL) is proposed to make inference for the accelerated failure time model without computing the limiting variance. Results from extensive simulation suggest that the JEL method outperforms the traditional normal approximation method in most cases. Subsequently, two real data sets are analyzed for illustration of the proposed method.
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页码:269 / 288
页数:19
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