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Biometrika 1983 70(2):421-431; doi:10.1093/biomet/70.2.421
© 1983 by Biometrika Trust
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Robust statistical analysis of interlaboratory studies

DAVID M. ROCKE

Graduate School of Administration, University of California Davis, California, U.S.A.

A common procedure in testing analytical methods is to send a portion of each of a number of samples to each of several laboratories. The results of such a study are submitted to statistical analysis to determine the two important variance components in the problem: replication error and laboratory bias. Outliers are relatively common in these data both among laboratory effects and among the residuals. This paper presents a method of analysis for interlaboratory studies that is robust to the existence of outliers and long-tailed distributions of random effects. Theoretical considerations as well as a Monte Carlo study are adduced as support for this new technique.

Key Words: Biweight estimate • Huber estimate • Least squares • Monte Carlo method • Outlier • Random effects • Robust estimation • Variance component


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