A0997
Title: Jackknife empirical likelihood inference for the accelerated failure time model
Authors: Yichuan Zhao - Georgia State University (United States) [presenting]
Abstract: The 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 inferences 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 (2007) used an induced smoothing approach, which smooths the estimating functions to obtain point and variance estimators. A more computationally efficient method called jackknife empirical likelihood (JEL) is proposed to make inferences 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 to illustrate the proposed method.
