Assessing a binary measurement system: A new plan using targeted verification with conditional sampling and baseline information
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We investigate efficient plans to assess the misclassification error rates of a binary measurement system used as an in-line inspection protocol. We assume that parts can be inspected repeatedly and that each part has its own (latent) misclassification rate. We propose a three-phase assessment plan. Phase I consists of data from recent inspection history. In Phase II, we select a sample of failed parts that we re-measure multiple times with the binary measurement system of interest. In Phase III, we verify a carefully selected subsample of the parts from Phase II with the aid of a binary gold standard measurement system. We show that the proposed plan is a substantial improvement over existing assessment plans in terms of cost and/or precision.
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Daniel E. Severn, Stefan H. Steiner, R. Jock MacKay (2019). Assessing a binary measurement system: A new plan using targeted verification with conditional sampling and baseline information. UWSpace. http://hdl.handle.net/10012/15394
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