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When no gold standard measurement system is available, we can assess a binary measurement system by making repeated measurements on a random sample of parts and then using a latent class model for the analysis. However, there is widespread criticism of the model assumptions that, given the true state of the part, the repeated measurements are independent and have the same misclassification probability. We propose a latent class random effects model that relaxes these assumptions by modeling the distribution of the two misclassification rates with Beta distributions. We start by finding the likelihood, the maximum likelihood estimates (MLEs) and their approximate standard deviations with the standard assessment plan that selects parts at random from the process. However, to estimate the model parameters with reasonable precision, the standard plan requires extremely large sample sizes in the common industrial situation where the proportion of conforming parts is high and the misclassification probabilities are small. More realistic sample sizes are possible when we instead sample randomly from the population of previously failed parts and supplement the likelihood with baseline information on the overall pass rate. We show using simulation that, for feasible designs, the asymptotic standard deviation based on the expected information provides a reasonably close approximation to the simulated standard deviation. We then use these approximations to investigate how the properties of the MLEs for the unknown parameters depend on the baseline size, the number of parts in the sample, and the number of repeated measurements per part.
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Stored only when both papers are in the corpus — 19 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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