Reliability estimation based on system data with an unknown load share rule

We consider a multicomponent load-sharing system in which the failure rate of a given component depends on the set of working components at any given time. Such systems can arise in software reliability models and in multivariate failure-time models in biostatistics, for example. A load-share rule dictates how stress or load is redistributed to the surviving components after a component fails within the system. In this paper, we assume the load share rule is unknown and derive methods for statistical inference on load-share parameters based on maximum likelihood. Components with ( individual) constant failure rates are observed in two environments: ( 1) the system load is distributed evenly among the working components, and ( 2) we assume only the load for each working component increases when other components in the system fail. Tests for these special load-share models are investigated.