We propose a Monte Carlo sampling algorithm for estimating guaranteed-coverage tolerance factors for normormal continuous distributions with known shape but unknown mean and variance. The algorithm is based on reformulating this root-finding problem as a quantile-estimation problem. Our quantile-estimation algorithm always converges, usually faster than stochastic-approximation algorithms, which are designed for general root-finding.
['Huifen Chen', 'Bruce W. Schmeiser']
Monte Carlo estimation for guaranteed-coverage nonnormal tolerance intervals