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An Empirical Likelihood Method for Semiparametric Linear Regression with Right Censored Data
Joint Authors
Li, Gang
Fang, Kai-Tai
Lu, Xuyang
Qin, Hong
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-03-14
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Fang, Kai-Tai& Li, Gang& Lu, Xuyang& Qin, Hong. 2013. An Empirical Likelihood Method for Semiparametric Linear Regression with Right Censored Data. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-473978
Modern Language Association (MLA)
Fang, Kai-Tai…[et al.]. An Empirical Likelihood Method for Semiparametric Linear Regression with Right Censored Data. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-473978
American Medical Association (AMA)
Fang, Kai-Tai& Li, Gang& Lu, Xuyang& Qin, Hong. An Empirical Likelihood Method for Semiparametric Linear Regression with Right Censored Data. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-473978
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-473978