Combining Information from Various Sources: A Prediction Problem and Other Industrial Applications

Archived Abstract of Former PSC Researcher

Hahn, G.J., and Trivellore Raghunathan. 1988. "Combining Information from Various Sources: A Prediction Problem and Other Industrial Applications." Technometrics, 30(1): 41-52.

Industrial problems frequently require estimates from various sources of information. For example, one may need to predict the tensile strength of a future bar from a particular casting based on limited data on other bars from that casting and extensive data on bars from other castings. Or one may wish to estimate the true viscosity of a batch of material based on a single measurement for the current batch, subject to appreciable measurement error, and similar readings on a large number of other batches. Simple weighting functions that use all of the data provide point estimates for these two problems, and a Bayesian framework yields associated interval estimates. Other applications and possible generalizations are also suggested.

Bayesian methods Kalman filter Pooling data Prediction intervals Random-effects models

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