An inverse-probability-weighted approach to the estimation of distribution function with doubly censored data

In this article, we propose an inverse-probability-weighted (IPW) estimator of distribution function for doubly censored data. The asymptotic properties of the IPW estimator are derived. A simulation study is conducted to compare the performance among the IPW estimator, a self-consistent estimator (SCE) and the nonparametric maximum likelihood estimator (NPMLE). Simulation results indicate that when censoring is not heavy, the performance of the IPW estimator is close to that of the SCE and NPMLE.