This paper provides an improved stopping boundary for open sequential selection of the normal population with the largest mean when all populations have common known variance. The proof relies on the theory of Brownian motion processes with drift.
Exposure assessment for food and drink consumption requires the combining of information about people's consumption of products with concentration data sets to provide predictions for chemical intake by humans. In this article, we present a method called nonparametric predictive inference (NPI) for exposure assessment. NPI is a distribution‐free method relying only on Hill's assumption . Effectively, is a postdata exchangeability assumption, which is a natural starting point for nonparametric statistics. For further discussion we refer to works by Hill and Coolen. We illustrate how NPI can be implemented to produce predictions for an individual's exposure based on consumption, body weight, and concentration data. NPI has the advantage that we do not have to assume a distribution to implement it. There may, however, be information available to suggest a distribution for a random quantity. Therefore, we present an NPI‐Bayes hybrid method where this information can be taken into account by using Bayesian methods while using NPI for the other random quantities in the model.
