An empirical likelihood method for semiparametric linear regression with right censored data
- PMID: 23573169
- PMCID: PMC3612471
- DOI: 10.1155/2013/469373
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.
References
-
- Cox DR. Regression models and life-tables. Journal of the Royal Statistical Society B. 1972;34(2):187–220.
-
- Andersen PK, Borgan O, Gill RD, Keiding N. Statistical Models Based on Counting Processes. New York, NY, USA: Springer; 1993.
-
- Buckley J, James I. Linear regression with censored data. Biometrika. 1979;66(3):429–436.
-
- Lai TL, Ying Z. Large sample theory of a modified Buckley-James estimator for regression analysis with censored data. Annals of Statistics. 1991;19:1370–1402.
-
- Ritov Y. Estimation in a linear regression model with censored data. Annals of Statistics. 1990;18:303–328.
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources
