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

Abstract

In this paper, empirical likelihood inferences for varying-coefficient single-index model with right-censored data are investigated. By a synthetic data approach, we propose an empirical log-likelihood ratio function for the index parameters, which are of primary interest, and show that its limiting distribution is a mixture of central chi-squared distributions. In order that the Wilks’ phenomenon holds, we propose an adjusted empirical log-likelihood ratio for the index parameters. The adjusted empirical log-likelihood is shown to have a standard chi-squared limiting distribution. Simulation studies are undertaken to assess the finite sample performance of the proposed confidence intervals. A real example is presented for illustration.