Nonparametric Predictive Inference for Acceptance Decisions

Abstract

This thesis presents new solutions for two acceptance decisions problems. First, we present methods for basic acceptance sampling for attributes, based on the nonparametric predictive inferential approach for Bernoulli data, which is extended for this application. We consider acceptance sampling based on destructive tests and on non-destructive tests. Attention is mostly restricted to single stage sampling, but extension to two-stage sampling is also considered and discussed.
Secondly, sequential acceptance decision problems are considered with the aim to select one or more candidates from a group, with the candidates observed sequentially, either per individual or in subgroups, and with the ordering of an individual compared to previous candidates and those in the same subgroup available. While, for given total group size, this problem can in principle be solved by dynamic programming, the computational effort required makes this not feasible for problems once the number of candidates to be selected, and the total group size are not small. We present a new heuristic approach to such problems, based on the principles of nonparametric predictive inference, and we study its performance via simulations.
The approach is very flexible and computationally straightforward, and has advantages over alternative heuristic rules that have been suggested in the literature.