New empirical likelihood inference for linear transformation models

The transformation model plays an important role in survival analysis. In this paper, we investigate the linear transformation model based on new empirical likelihood. Motivated by Fine et al. (1998) and Yu et al. (2011), we introduce the truncated survival time t0t0 and adjust each term of estimating equations to improve the accuracy of coverage probability. We prove that the log-likelihood ratio has the asymptotic distribution 4χp+12. The new empirical likelihood method avoids estimating the complicated covariance matrix in contrast to normal approximation method and empirical likelihood method developed by Zhao (2010). Moreover, the proposed method enables us to obtain confidence intervals for the component of regression parameters. In the simulation study, our method demonstrates better performance than the traditional method in the small samples.