Robust estimation and bias-corrected empirical likelihood in generalized linear models with right censored data.

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 χ2 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 th

SUBMITTER: Xue L 

PROVIDER: S-EPMC11328815 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature