Monte carlo methods for confidence interval estimation using the bootstrap technique

An extension of Monte Carlo methods to confidence interval estimation, using the bootstrap technique, is investigated. The approach may have considerable potential for parameters that have estimators with complicated analytic properties but with probability distributions that can be simulated. Potential fields of application include ratio estimation, compound distributions and estimation of probabilities.