Date of Award
7-17-2010
Degree Type
Closed Thesis
Degree Name
Master of Science (MS)
Department
Mathematics and Statistics
First Advisor
Yichuan Zhao - Chair
Second Advisor
Xu Zhang
Third Advisor
Yuanhui Xiao
Fourth Advisor
Yu-Sheng Hsu
Abstract
In this thesis, we study two methods for inference of parameters in the accelerated failure time model with right censoring data. One is the Wald-type method, which involves parameter estimation. The other one is empirical likelihood method, which is based on the asymptotic distribution of likelihood ratio. We employ a monotone censored data version of Kendall estimating equation, and construct confidence intervals from both methods. In the simulation studies, we compare the empirical likelihood (EL) and the Wald-type procedure in terms of coverage accuracy and average length of confidence intervals. It is concluded that the empirical likelihood method has a better performance. We also compare the EL for Kendall’s rank regression estimator with the EL for other well known estimators and find advantages of the EL for Kendall estimator for small size sample. Finally, a real clinical trial data is used for the purpose of illustration.
DOI
https://doi.org/10.57709/1059732
Recommended Citation
Lu, Yinghua, "Empirical Likelihood Inference for the Accelerated Failure Time Model via Kendall Estimating Equation." Thesis, Georgia State University, 2010.
doi: https://doi.org/10.57709/1059732