Empirical likelihood for single-index varying-coefficient models with right-censored data

This paper is concerned with an estimation procedure of a class of single-index varying-coefficient models with right-censored data. An adjusted empirical log-likelihood ratio for the index parameters, which are of primary interest, is proposed using a synthetic data approach. The adjusted empirical likelihood is shown to have a standard chi-squared limiting distribution. Furthermore, we increase the accuracy of the proposed confidence regions by using the constraint that the index is of norm 1. Simulation studies are carried out to highlight the performance of the proposed method compared with the traditional normal approximation method.