Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint

We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does not exceed an upper bound. The outcome distributions are not known. We construct a class of consistent adaptive policies, under which the average outcome converges with probability 1 to the true value under complete information for all distributions with finite means. We also compare the rate of convergence for various policies in this class using simulation

Item-by-item sampling for promotional purposes

This is an accepted manuscript of an article accepted for publication by Taylor & Francis in Quality Technology & Quantitative Management on 7 June 2017, available online at doi: http://www.tandfonline.com/doi/abs/10.1080/16843703.2017.1335494In this paper we present a method for sampling items that are checked on a pass/fail basis, with a view to a statement being made about the success/failure rate for the purposes of promoting an organisation’s product/service to potential clients/customers. Attention is paid to the appropriate use of statistical phrases for the statements and this leads to the use of Bayesian credible intervals, thus it exceeds what can achieved with standard acceptance sampling techniques. The hypergeometric distribution is used to calculate successive stopping rules so that the resources used for sampling can be minimised. Extensions to the sampling procedure are considered to allow the potential for stronger and weaker statements to be made as sampling progresses. The relationship between the true error rate and the probabilities of making correct statements is discussed.Peer reviewedFinal Accepted Versio

Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint

We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does not exceed an upper bound. The outcome distributions are not known. We construct a class of consistent adaptive policies, under which the average outcome converges with probability 1 to the true value under complete information for all distributions with finite means. We also compare the rate of convergence for various policies in this class using simulation. © 2012, Springer Science+Business Media New York