title: An empirical likelihood method for semiparametric linear regression with right censored data.
abstract: 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.
authors.affiliation: Beijing Normal University-Hong Kong Baptist University, United International College, Zhuhai 519085, China.
authors.forename: Kai-Tai
authors.initials: KT
authors.lastname: Fang
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.forename: Xuyang
authors.initials: X
authors.lastname: Lu
authors.forename: Hong
authors.initials: H
authors.lastname: Qin
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016027:Heart Transplantation / Q000379:methods; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D011336:Probability; D012044:Regression Analysis; D016019:Survival Analysis