Cost-Optimal Multistage Sampling Plans in Statistical Quality Control
Multistage Bayesian decision procedures in statistical quality control are known from attribute sampling. In this paper they are introduced in a more general framework occuring in lot-control by using the theory of Bayesian sequentially planned decision procedures. We show that under sufficiency and transitivity assumptions and monotonicity properties concerning the distributionand cost set-up these Bayes-procedures have(z,c-,c+)-structure which, on one hand, generalizes results of K.-H. Waldmann and, on the other hand, reduces computational effort significantly. Finally, examples taken from attribute sampling and life testing for an outgoing lot are presented.
Acceptance sampling; backward induction; Bayes procedures; multistage decision procedures; quality control