Tolerance limits on order statistics in future samples coming from the Pareto distribution

Abstract

It is often desirable to have statistical tolerance limits available for the distributions used to describe time-to-failure data in reliability problems. For example, one might wish to know if at least a certain proportion, say β, of a manufactured product will operate at least T hours. This question cannot usually be answered exactly, but it may be possible to determine a lower tolerance limit L(X), based on a random sample X, such that one can say with a certain confidence γ that at least 100β% of the product will operate longer than L(X). Then reliability statements can be made based on L(X), or, decisions can be reached by comparing L(X) to T. Tolerance limits of the type mentioned above are considered in this paper, which presents a new approach to constructing lower and upper tolerance limits on order statistics in future samples. Attention is restricted to invariant families of distributions under parametric uncertainty. The approach used here emphasizes pivotal quantities relevant for obtaining tolerance factors and is applicable whenever the statistical problem is invariant under a group of transformations that acts transitively on the parameter space. It does not require the construction of any tables and is applicable whether the past data are complete or Type II censored. The proposed approach requires a quantile of the F distribution and is conceptually simple and easy to use. For illustration, the Pareto distribution is considered. The discussion is restricted to one-sided tolerance limits. A practical example is given.