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Abstract
The statistical evaluation of the reliability of binary tests and inspections is a challenging endeavor. In this paper, we propose an approach for the common situation where the true condition of the inspected items is unobservable (“gold-standard unavailable”), the probabilities of false acceptance and false rejection vary across items, and rejections are relatively rare. Our approach fits a latent-variable model, where the variability in misclassification probabilities is driven by a continuous property of a part. To deal with the low prevalence of rejections, we propose sampling items from multiple sources. The performance and properties of the estimators are assessed using simulation, asymptotic approximations, and a real-life case at a car-parts manufacturer.
Additional information
Notes on contributors
Tashi P. Erdmann
Dr. Erdmann is a Statistical Consultant in the Statistics & Chemometrics team at Shell Global Solutions International. His email address is [email protected].
Thomas S. Akkerhuis
Mr. Akkerhuis is Consultant and PhD Student at IBIS UvA. His email address is [email protected].
Jeroen De Mast
Dr. De Mast is Principal Consultant and Professor of Methods and Statistics for Operations Management at IBIS UvA. His email address is [email protected].
Stefan H. Steiner
Dr. Steiner is Professor and Chair in the Department of Statistics and Actuarial Science, and Director of the Business and Industrial Statistics Research Group at the University of Waterloo. His email address is [email protected].