Back to Search
Start Over
Robust estimation and bias-corrected empirical likelihood in generalized linear models with right censored data.
- Source :
-
Journal of Applied Statistics . Sep2024, Vol. 51 Issue 11, p2197-2213. 17p. - Publication Year :
- 2024
-
Abstract
- In this paper, we study the robust estimation and empirical likelihood for the regression parameter in generalized linear models with right censored data. A robust estimating equation is proposed to estimate the regression parameter, and the resulting estimator has consistent and asymptotic normality. A bias-corrected empirical log-likelihood ratio statistic of the regression parameter is constructed, and it is shown that the statistic converges weakly to a standard $ \chi ^2 $ χ 2 distribution. The result can be directly used to construct the confidence region of regression parameter. We use the bias correction method to directly calibrate the empirical log-likelihood ratio, which does not need to be multiplied by an adjustment factor. We also propose a method for selecting the tuning parameters in the loss function. Simulation studies show that the estimator of the regression parameter is robust and the bias-corrected empirical likelihood is better than the normal approximation method. An example of a real dataset from Alzheimer's disease studies shows that the proposed method can be applied in practical problems. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02664763
- Volume :
- 51
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Journal of Applied Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 179069923
- Full Text :
- https://doi.org/10.1080/02664763.2023.2277117