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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 469373, 9 pages
Research Article

An Empirical Likelihood Method for Semiparametric Linear Regression with Right Censored Data

1Beijing Normal University-Hong Kong Baptist University, United International College, Zhuhai 519085, China
2Department of Biostatistics, School of Public Health, University of California, Los Angeles, CA 90095-1772, USA
3Department of Statistics, Central China Normal University, Wuhan 430079, China

Received 5 November 2012; Revised 29 January 2013; Accepted 11 February 2013

Academic Editor: Xiaonan Xue

Copyright © 2013 Kai-Tai Fang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper develops a new empirical likelihood method for semiparametric linear regression with a completely unknown error distribution and right censored survival data. The method is based on the Buckley-James (1979) estimating equation. It inherits some appealing properties of the complete data empirical likelihood method. For example, it does not require variance estimation which is problematic for the Buckley-James estimator. We also extend our method to incorporate auxiliary information. We compare our method with the synthetic data empirical likelihood of Li and Wang (2003) using simulations. We also illustrate our method using Stanford heart transplantation data.