Estimating general variable acceptance sampling plans by bootstrap methods

We consider variable acceptance sampling plans that control the lot or process fraction defective, where a specification limit defines acceptable quality. The problem is to find a sampling plan that fulfils some conditions, usually on the operation characteristic. Its calculation heavily depends on distributional properties that, in practice, might be doubtful. If prior data are already available, we propose to estimate the sampling plan by means of bootstrap methods. The bias and standard error of the estimated plan can be assessed easily by Monte Carlo approximation to the respective bootstrap moments. This resampling approach does not require strong assumptions and, furthermore, is a flexible method that can be extended to any statistic that might be informative for the fraction defective in a lot.