Foreword: Special issue on statistics in quality and productivity (2018)

This special issue of Applied Stochastic Models in Business and Industry contains five papers that were presented at the 2017 Quality and Productivity Research Conference (QPRC) in Storrs, Connecticut (USA), hosted by the University of Connecticut. The QPRC is an annual conference (held since 1984) sponsored by the Quality and Productivity Section of the American Statistical Association (ASA). Its mission is to improve the quality of products and services and the efficiency of industries by stimulating the research and development of better statistical methods and to identify new application areas where statistics can have a significant impact. The 2019 QPRC will be held on June 10-13 in Washington, DC (USA), hosted by the American University (see https://www.american.edu/cas/qprc/). Additional information about QPRC, including links to websites of conferences held since 1999, can be found in the ASA Quality and Productivity community page: http://community.amstat.org/qp/events/qualityandproductivityresearchconference. The first paper of the issue, by Kevin Quinlan and Christine Anderson-Cook, discusses the problem of design of experiments for logistic regression models with the objective of evaluating performance of forensic algorithms. They show how a Bayesian approach is useful in this context and illustrate the application of their methodology using a forensic case study and several examples. The second paper, by Haim Bar, James Booth, Martin Wells, and Kangyan Liu, presents an approach to classification problems when the number of predictors is larger than the sample size. They show how the initial screening step is of value under the conditions of sparsity and compare performance of several classifiers in this setting. They illustrate the properties of the proposed approach using simulation and two case studies. The third paper, by Aleksey Polunchenko and Vasanthan Raghavan, compares the performance of the Cumulative Sum (CUSUM) chart and the Shiryaev-Roberts procedure for detecting changes in autocorrelated data. Although, asymptotically, both procedures possess a second-order optimality property for the selected comparison criterion, CUSUM was showing a somewhat better performance for all values of parameters used in the simulation study. The fourth paper, by Gabriel Odom, Kathryn Newhart, Tzahi Cath, and Amanda Hering, discusses the problem of multivariate statistical process monitoring when the data generating system can assume multiple states. They illustrate ways in which information about the current state of the monitored system can be used to improve problem detection and diagnosis. Finally, the fifth paper, by Muzaffer Musal and Tahir Ekin, presents an iterative sampling framework to estimate the proportion and number of overpaid claims and the overpayment recovery amount in a health care organization. The novel sampling resource allocation scheme proposed in the paper should be instrumental in the ongoing effort to reduce waste in health care systems and make them more efficient. We deeply appreciate the efforts of the QPRC-2017 Program Committee and Organizing Committee members, leading to this highly successful and well-attended event. We are also most thankful to its participants and sponsors for their generous investment of time and resources and to our editors and referees for their work on producing this special issue.