Conditional lifetime data analysis using the limited expected value function

Much failure, and other event, data are commonly highly censored. Consequently this limits the efficacy of many statistical analysis techniques. The limited expected value (LEV) function presents an alternative way of characterizing lifetime distributions. In essence the LEV provides a means of calculating a truncated mean time to failure (or mean time before failure if appropriate) that is adjusted at each of the censoring times and so appears potentially suitable for dealing with censored data structures. In theory, the LEV has been defined for many standard distributions, however its practical use is not well developed. This paper aims to extend the theory of LEV for typical censoring structures to develop procedures that will assist in model identification as well as parameter estimation. Applications to typical event data will be presented and the use of LEV in comparison with a selection of existing lifetime distributional analysis will be based on some preliminary research.