Estimation of the survival function with increasing failure rate based on left truncated and right censored data

Abstract

The maximum conditional likelihood estimator of the survival function with increasing hazard rate is derived, based on left truncated and right censored data. This estimator always exists, whereas the fully nonparametric conditional maximum likelihood estimator may not exist. A strong consistency theorem is established based on the total time on test transformation.