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Abstract
In this article we consider a Bayesian approach to inference in which there is a calibration relationship between measured and true quantities of interest. One situation in which this approach is useful is for unknowns in which calibration intervals are obtained. The other situation is when inference about a population is desired in which tolerance intervals are produced. The Bayesian approach easily handles a general calibration relationship, say nonlinear, with nonnormal errors. The population may also be general, say lognormal, for quantities which are nonnegative. The Bayesian approach is illustrated with three examples and implemented with the freely available WinBUGS software.
Additional information
Notes on contributors
M. Hamada
Dr. Hamada is a Technical Staff Member in Statistical Sciences. His email address is [email protected].
A. Pohl
Capt. Pohl is an Instructor of Statistics in the Department of Mathematics and Statistics.
C. Spiegelman
Dr. Spiegelman is a Professor in the Department of Statistics and the Texas Transportation Institute.
J. Wendelberger
Dr. Wendelberger is a Technical Staff Member in Statistical Sciences.