Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1155/2013/469373 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE |
Keywords | Field | DocType |
linear models,probability,monte carlo method,algorithms,computer simulation,regression analysis,survival analysis | Econometrics,Likelihood function,Regression analysis,Computer science,Linear model,Empirical likelihood,Statistics,Restricted maximum likelihood,Censoring (statistics),Estimator,Estimating equations | Journal |
Volume | Issue | ISSN |
2013 | null | 1748-670X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai-Tai Fang | 1 | 165 | 23.65 |
| Gang Li | 2 | 6 | 2.82 |
| Xuyang Lu | 3 | 0 | 0.34 |
| Hong Qin | 4 | 7 | 3.54 |