Abstract
A method for estimation of the measurement error probability density from three or more measurements on each of several dissimilar items is presented. Differences between measurements on the same item provide estimates of the densities of the first and second differences between error realizations. The relations between these densities and the error density, expressed in terms of characteristic functions and Hermite function expansions, are the basis for a nonlinear least-squares algorithm. Estimated percentiles of the error density are investigated by Monte Carlo experiments. The method is applied to measurements by several laboratories on an inhomogeneous reference material with asymmetric variability.
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